Publications
Monographs

A. Bayandina, P. Dvurechensky, A. Gasnikov, Mirror descent and convex optimization problems with nonsmooth inequality constraints, in: Large Scale and Distributed Optimization, P. Giselsson, A. Rantzer, eds., Lecture Notes in Mathematics 2227, Springer Nature Switzerland AG, Cham, 2018, pp. 181215, (Chapter Published), DOI 10.1007/9783319974781_8 .

D. Belomestny, J. Schoenmakers, Advanced SimulationBased Methods for Optimal Stopping and Control: With Applications in Finance, Macmillan Publishers Ltd., London, 2018, 364 pages, (Monograph Published), DOI 10.1057/9781137033512 .

H.G. Bartel, H.J. Mucha, Chapter 2: Incomparability/Inequality Measures and Clustering, in: Partial Order Concepts in Applied Sciences, M. Fattore, R. Brüggemann, eds., Springer International Publishing, New York, 2017, pp. 2134, (Chapter Published).
Articles in Refereed Journals

W. Dreyer, P. Friz, P. Gajewski, C. Guhlke, M. Maurelli, Stochastic manyparticle model for LFP electrodes, Continuum Mechanics and Thermodynamics, 30 (2018), pp. 593628, DOI 10.1007/s0016101806297 .
Abstract
In the framework of nonequilibrium thermodynamics we derive a new model for porous electrodes. The model is applied to LiFePO4 (LFP) electrodes consisting of many LFP particles of nanometer size. The phase transition from a lithiumpoor to a lithiumrich phase within LFP electrodes is controlled by surface fluctuations leading to a system of stochastic differential equations. The model is capable to derive an explicit relation between battery voltage and current that is controlled by thermodynamic state variables. This voltagecurrent relation reveals that in thin LFP electrodes lithium intercalation from the particle surfaces into the LFP particles is the principal rate limiting process. There are only two constant kinetic parameters in the model describing the intercalation rate and the fluctuation strength, respectively. The model correctly predicts several features of LFP electrodes, viz. the phase transition, the observed voltage plateaus, hysteresis and the rate limiting capacity. Moreover we study the impact of both the particle size distribution and the active surface area on the voltagecharge characteristics of the electrode. Finally we carefully discuss the phase transition for varying charging/discharging rates. 
P. Pigato, Tube estimates for diffusion processes under a weak Hörmander condition, Annales de l'Institut Henri Poincare. Probabilites et Statistiques, 54 (2018), pp. 299342, DOI 10.1214/16AIHP805 .
Abstract
We consider a diffusion process under a local weak Hörmander condition on the coefficients. We find Gaussian estimates for the density in short time and exponential lower and upper bounds for the probability that the diffusion remains in a small tube around a deterministic trajectory (skeleton path). These bounds depend explicitly on the radius of the tube and on the energy of the skeleton path. We use a norm which reflects the nonisotropic structure of the problem, meaning that the diffusion propagates in R2 with different speeds in the directions ? and [?,b]. We establish a connection between this norm and the standard control distance. 
M. Redmann, P. Kürschner, An output error bound for timelimited balanced truncation, Systems & Control Letters, 121 (2018), pp. 16, DOI 10.1016/j.sysconle.2018.08.004 .
Abstract
When solving partial differential equations numerically, usually a high order spatial discretization is needed. Model order reduction (MOR) techniques are often used to reduce the order of spatiallydiscretized systems and hence reduce computational complexity. A particular MOR technique to obtain a reduced order model (ROM) is balanced truncation (BT). However, if one aims at finding a good ROM on a certain finite time interval only, timelimited BT (TLBT) can be a more accurate alternative. So far, no error bound on TLBT has been proved. In this paper, we close this gap in the theory by providing an output error bound for TLBT with two different representations. The performance of the error bound is then shown in several numerical experiments. 
M. Redmann, Energy estimates and model order reduction for stochastic bilinear systems, International Journal of Control, (2018), Published online: 8.11.2018, DOI 10.1080/00207179.2018.1538568 .
Abstract
In this paper, we investigate a largescale stochastic system with bilinear drift and linear diffusion term. Such high dimensional systems appear for example when discretizing a stochastic partial differential equations in space. We study a particular model order reduction technique called balanced truncation (BT) to reduce the order of spatiallydiscretized systems and hence reduce computational complexity. We introduce suitable Gramians to the system and prove energy estimates that can be used to identify states which contribute only very little to the system dynamics. When BT is applied the reduced system is obtained by removing these states from the original system. The main contribution of this paper is an L2error bound for BT for stochastic bilinear systems. This result is new even for deterministic bilinear equations. In order to achieve it, we develop a new technique which is not available in the literature so far. 
M. Redmann, Type II balanced truncation for deterministic bilinear control systems, SIAM Journal on Control and Optimization, 56 (2018), pp. 25932612, DOI 10.1137/17M1147962 .
Abstract
When solving partial differential equations numerically, usually a high order spatial discretisation is needed. Model order reduction (MOR) techniques are often used to reduce the order of spatiallydiscretised systems and hence reduce computational complexity. A particular MOR technique to obtain a reduced order model (ROM) is balanced truncation (BT), a method which has been extensively studied for deterministic linear systems. As socalled type I BT it has already been extended to bilinear equations, an important subclass of nonlinear systems. We provide an alternative generalisation of the linear setting to bilinear systems which is called type II BT. The Gramians that we propose in this context contain information about the control. It turns out that the new approach delivers energy bounds which are not just valid in a small neighbourhood of zero. Furthermore, we provide an ℋ∞error bound which so far is not known when applying type I BT to bilinear systems. 
M. Redmann, Type II singular perturbation approximation for linear systems with Lévy noise, SIAM Journal on Control and Optimization, 56 (2018), pp. 21202158, DOI 10.1137/17M113160X .
Abstract
When solving linear stochastic partial differential equations numerically, usually a high order spatial discretisation is needed. Model order reduction (MOR) techniques are often used to reduce the order of spatiallydiscretised systems and hence reduce computational complexity. A particular MOR technique to obtain a reduced order model (ROM) is singular perturbation approximation (SPA), a method which has been extensively studied for deterministic systems. As socalled type I SPA it has already been extended to stochastic equations. We provide an alternative generalisation of the deterministic setting to linear systems with Lévy noise which is called type II SPA. It turns out that the ROM from applying type II SPA has better properties than the one of using type I SPA. In this paper, we provide new energy interpretations for stochastic reachability Gramians, show the preservation of mean square stability in the ROM by type II SPA and prove two different error bounds for type II SPA when applied to Lévy driven systems 
M. Redmann, Type II singular perturbation approximation for linear systems with Lévy noise, SIAM Journal on Control and Optimization, 56 (2018), pp. 21202158, DOI 10.1137/17M113160X .
Abstract
When solving linear stochastic partial differential equations numerically, usually a high order spatial discretisation is needed. Model order reduction (MOR) techniques are often used to reduce the order of spatiallydiscretised systems and hence reduce computational complexity. A particular MOR technique to obtain a reduced order model (ROM) is singular perturbation approximation (SPA), a method which has been extensively studied for deterministic systems. As socalled type I SPA it has already been extended to stochastic equations. We provide an alternative generalisation of the deterministic setting to linear systems with Lévy noise which is called type II SPA. It turns out that the ROM from applying type II SPA has better properties than the one of using type I SPA. In this paper, we provide new energy interpretations for stochastic reachability Gramians, show the preservation of mean square stability in the ROM by type II SPA and prove two different error bounds for type II SPA when applied to Lévy driven systems 
M.A. Freitag, M. Redmann, Balanced model order reduction for linear random dynamical systems driven by Lévy noise, Journal of Computational Dynamics, 5 (2018), pp. 3359, DOI 10.3934/jcd.2018002 .

D. Belomestny, J.G.M. Schoenmakers, Projected particle methods for solving McKeanVlaslov equations, SIAM Journal on Numerical Analysis, 56 (2018), pp. 31693195, DOI 10.1137/17M1111024 .
Abstract
We propose a novel projectionbased particle method for solving McKeanVlasov stochastic differential equations. Our approach is based on a projectiontype estimation of the marginal density of the solution in each time step. The projectionbased particle method leads in many situations to a significant reduction of numerical complexity compared to the widely used kernel density estimation algorithms. We derive strong convergence rates and rates of density estimation. The convergence analysis, particularly in the case of linearly growing coefficients, turns out to be rather challenging and requires some new type of averaging technique. This case is exemplified by explicit solutions to a class of McKeanVlasov equations with affine drift. The performance of the proposed algorithm is illustrated by several numerical examples. 
K. Chouk, P. Friz, Support theorem for a singular SPDE: The case of gPAM, Annales de l'Institut Henri Poincare. Probabilites et Statistiques, 54 (2018), pp. 202219.
Abstract
We consider the generalized parabolic Anderson equation (gPAM) in 2 dimensions with periodic boundary. This is an example of a singular semilinear stochastic partial differential equation in the subcritical regime, with (renormalized) solutions only recently understood via Hairer?s regularity structures and, in some cases equivalently, paracontrollled distributions by Gubinelli, Imkeller and Perkowski. In the present paper we utilise the paracontrolled machinery and obtain a (Stroock?Varadhan) type support description for the law of gPAM. In the spirit of rough paths, the crucial step is to identify the support of the enhanced noise in a sufficiently fine topology. The renormalization is seen to affect the support description. 
A. Gasnikov, P. Dvurechensky, M. Zhukovskii, S. Kim, S. Plaunov, D. Smirnov, F. Noskov, About the power law of the PageRank vector distribution. Part 2. BackleyOsthus model, power law verification for this model and setup of real search engines, Numerical Analysis and Applications, 11 (2018), pp. 1632, DOI 10.1134/S1995423918010032 .

B. Hofmann, P. Mathé, Tikhonov regularization with oversmoothing penalty for nonlinear illposed problems in Hilbert scales, Inverse Problems. An International Journal on the Theory and Practice of Inverse Problems, Inverse Methods and Computerized Inversion of Data, 34 (2018), pp. 015007/1015007/14.

V. Krätschmer, M. Ladkau, R.J.A. Laeven, J.G.M. Schoenmakers, M. Stadje, Optimal stopping under uncertainty in drift and jump intensity, Mathematics of Operations Research, (2018), published online on 09.08.2018, urlhttps://doi.org/101287/moor.2017.0899., DOI 10.1287/moor.2017.0899 .
Abstract
This paper studies the optimal stopping problem in the presence of model uncertainty (ambiguity). We develop a method to practically solve this problem in a general setting, allowing for general timeconsistent ambiguity averse preferences and general payoff processes driven by jumpdiffusions. Our method consists of three steps. First, we construct a suitable Doob martingale associated with the solution to the optimal stopping problem %represented by the Snell envelope using backward stochastic calculus. Second, we employ this martingale to construct an approximated upper bound to the solution using duality. Third, we introduce backwardforward simulation to obtain a genuine upper bound to the solution, which converges to the true solution asymptotically. We analyze the asymptotic behavior and convergence properties of our method. We illustrate the generality and applicability of our method and the potentially significant impact of ambiguity to optimal stopping in a few examples. 
A. Lejay, P. Pigato, Statistical estimation of the oscillating Brownian motion, Bernoulli. Official Journal of the Bernoulli Society for Mathematical Statistics and Probability, 24 (2018), pp. 35683602, DOI 10.3150/17BEJ969 .
Abstract
We study the asymptotic behavior of estimators of a twovalued, discontinuous diffusion coefficient in a Stochastic Differential Equation, called an Oscillating Brownian Motion. Using the relation of the latter process with the Skew Brownian Motion, we propose two natural consistent estimators, which are variants of the integrated volatility estimator and take the occupation times into account. We show the stable convergence of the renormalized errors? estimations toward some Gaussian mixture, possibly corrected by a term that depends on the local time. These limits stem from the lack of ergodicity as well as the behavior of the local time at zero of the process. We test both estimators on simulated processes, finding a complete agreement with the theoretical predictions. 
I. Silin, V. Spokoiny, Bayesian inference for spectral projectors of covariance matrix, Electronic Journal of Statistics, 12 (2018), pp. 19481987, DOI 10.1214/18EJS1451 .

CH. Bayer, P. Friz, A. Gulisashvili, B. Horvath, B. Stemper, Shorttime nearthemoney skew in rough fractional volatility models, Quantitative Finance, published online on 13.11.2018, DOI 10.1080/14697688.2018.1529420 .
Abstract
We consider rough stochastic volatility models where the driving noise of volatility has fractional scaling, in the "rough" regime of Hurst parameter H < ½. This regime recently attracted a lot of attention both from the statistical and option pricing point of view. With focus on the latter, we sharpen the large deviation results of FordeZhang (2017) in a way that allows us to zoomin around the money while maintaining full analytical tractability. More precisely, this amounts to proving higher order moderate deviation estimates, only recently introduced in the option pricing context. This in turn allows us to push the applicability range of known atthemoney skew approximation formulae from CLT type logmoneyness deviations of order t^{1/2} (recent works of Alòs, León & Vives and Fukasawa) to the wider moderate deviations regime. 
CH. Bayer, H. Mai, J.G.M. Schoenmakers, Forwardreverse expectationmaximization algorithm for Markov chains: Convergence and numerical analysis, Advances in Applied Probability, 2 (2018), pp. 621644, DOI 10.1017/apr.2018.27 .
Abstract
We develop a forwardreverse expectationmaximization (FREM) algorithm for estimating parameters of a discretetime Markov chain evolving through a certain measurable statespace. For the construction of the FREM method, we develop forwardreverse representations for Markov chains conditioned on a certain terminal state. We prove almost sure convergence of our algorithm for a Markov chain model with curved exponential family structure. On the numerical side, we carry out a complexity analysis of the forwardreverse algorithm by deriving its expected cost. Two application examples are discussed. 
P. Dvurechensky, A. Gasnikov, A. Lagunovskaya, Parallel algorithms and probability of large deviation for stochastic convex optimization problems, Numerical Analysis and Applications, 11 (2018), pp. 3337, DOI 10.1134/S1995423918010044 .

P. Friz, H. Zhang, Differential equations driven by rough paths with jumps, Journal of Differential Equations, 264 (2018), pp. 62266301, DOI 10.1016/j.jde.2018.01.031 .
Abstract
We develop the rough path counterpart of Itô stochastic integration and differential equations driven by general semimartingales. This significantly enlarges the classes of (Itô/forward) stochastic differential equations treatable with pathwise methods. A number of applications are discussed. 
TH. Koprucki, M. Kohlhase, K. Tabelow, D. Müller, F. Rabe, Model pathway diagrams for the representation of mathematical models, Optical and Quantum Electronics, 50 (2018), pp. 70/170/9, DOI 10.1007/s1108201813217 .
Abstract
Mathematical models are the foundation of numerical simulation of optoelectronic devices. We present a concept for a machineactionable as well as humanunderstandable representation of the mathematical knowledge they contain and the domainspecific knowledge they are based on. We propose to use theory graphs to formalize mathematical models and model pathway diagrams to visualize them. We illustrate our approach by application to the van Roosbroeck system describing the carrier transport in semiconductors by drift and diffusion. We introduce an approach for the blockbased composition of models from simpler components. 
F. Anker, Ch. Bayer, M. Eigel, M. Ladkau, J. Neumann, J.G.M. Schoenmakers, SDE based regression for random PDEs, SIAM Journal on Scientific Computing, 39 (2017), pp. A1168A1200.
Abstract
A simulation based method for the numerical solution of PDE with random coefficients is presented. By the FeynmanKac formula, the solution can be represented as conditional expectation of a functional of a corresponding stochastic differential equation driven by independent noise. A time discretization of the SDE for a set of points in the domain and a subsequent Monte Carlo regression lead to an approximation of the global solution of the random PDE. We provide an initial error and complexity analysis of the proposed method along with numerical examples illustrating its behaviour. 
F. Anker, Ch. Bayer, M. Eigel, J. Neumann, J.G.M. Schoenmakers, A fully adaptive interpolated stochastic sampling method for linear random PDEs, International Journal for Uncertainty Quantification, 7 (2017), pp. 189205, DOI 10.1615/Int.J.UncertaintyQuantification.2017019428 .
Abstract
A numerical method for the fully adaptive sampling and interpolation of PDE with random data is presented. It is based on the idea that the solution of the PDE with stochastic data can be represented as conditional expectation of a functional of a corresponding stochastic differential equation (SDE). The physical domain is decomposed subject to a nonuniform grid and a classical Euler scheme is employed to approximately solve the SDE at grid vertices. Interpolation with a conforming finite element basis is employed to reconstruct a global solution of the problem. An a posteriori error estimator is introduced which provides a measure of the different error contributions. This facilitates the formulation of an adaptive algorithm to control the overall error by either reducing the stochastic error by locally evaluating more samples, or the approximation error by locally refining the underlying mesh. Numerical examples illustrate the performance of the presented novel method. 
M. Redmann, P. Benner, Singular perturbation approximation for linear systems with Lévy noise, Stochastics and Dynamics, published online on 10.08.2017, urlhttps://doi.org/10.1142/S0219493718500338, DOI 10.1142/S0219493718500338 .
Abstract
To solve a stochastic linear evolution equation numerically, finite dimensional approximations are commonly used. For a good approximation, one might end up with a sequence of ordinary stochastic linear equations of high order. To reduce the high dimension for practical computations, we consider the singular perturbation approximation as a model order reduction technique in this paper. This approach is wellknown from deterministic control theory and here we generalize it for controlled linear systems with Lévy noise. Additionally, we discuss properties of the reduced order model, provide an error bound, and give some examples to demonstrate the quality of this model order reduction technique. 
A. Andresen, V. Spokoiny, Convergence of an alternating maximization procedure, Journal of Machine Learning Research (JMLR). MIT Press, Cambridge, MA. English, English abstracts., 53 (2017), pp. 389429, DOI 10.1214/15AIHP720 .

A. Anikin, A. Gasnikov, P. Dvurechensky, A. Turin, A. Chernov, Dual approaches to the minimization of strongly convex functionals with a simple structure under affine constraints, Computational Mathematics and Mathematical Physics, 57 (2017), pp. 12621276.

D. Belomestny, R. Hildebrand, J.G.M. Schoenmakers, Optimal stopping via pathwise dual empirical maximisation, Applied Mathematics and Optimization. An International Journal with Applications to Stochastics, pp. published online on 08.11.2017, urlhttps://doi.org/10.1007/s0024501794549, DOI 10.1007/s0024501794549 .
Abstract
The optimal stopping problem arising in the pricing of American options can be tackled by the so called dual martingale approach. In this approach, a dual problem is formulated over the space of martingales. A feasible solution of the dual problem yields an upper bound for the solution of the original primal problem. In practice, the optimization is performed over a finitedimensional subspace of martingales. A sample of paths of the underlying stochastic process is produced by a MonteCarlo simulation, and the expectation is replaced by the empirical mean. As a rule the resulting optimization problem, which can be written as a linear program, yields a martingale such that the variance of the obtained estimator can be large. In order to decrease this variance, a penalizing term can be added to the objective function of the pathwise optimization problem. In this paper, we provide a rigorous analysis of the optimization problems obtained by adding different penalty functions. In particular, a convergence analysis implies that it is better to minimize the empirical maximum instead of the empirical mean. Numerical simulations confirm the variance reduction effect of the new approach. 
D. Belomestny, H. Mai, J.G.M. Schoenmakers, Generalized PostWidder inversion formula with application to statistics, Journal of Mathematical Analysis and Applications, 455 (2017), pp. 89104.
Abstract
In this work we derive an inversion formula for the Laplace transform of a density observed on a curve in the complex domain, which generalizes the well known PostWidder formula. We establish convergence of our inversion method and derive the corresponding convergence rates for the case of a Laplace transform of a smooth density. As an application we consider the problem of statistical inference for variancemean mixture models. We construct a nonparametric estimator for the mixing density based on the generalized PostWidder formula, derive bounds for its root mean square error and give a brief numerical example. 
S. Bürger, P. Mathé, Discretized Lavrent'ev regularization for the autoconvolution equation, Applicable Analysis. An International Journal, 96 (2017), pp. 16181637, DOI 10.1080/00036811.2016.1212336 .
Abstract
Lavrent?ev regularization for the autoconvolution equation was considered by Janno J. in Lavrent?ev regularization of illposed problems containing nonlinear neartomonotone operators with application to autoconvolution equation, Inverse Prob. 2000;16:333?348. Here this study is extended by considering discretization of the Lavrent?ev scheme by splines. It is shown how to maintain the known convergence rate by an appropriate choice of spline spaces and a proper choice of the discretization level. For piecewise constant splines the discretized equation allows for an explicit solver, in contrast to using higher order splines. This is used to design a fast implementation by means of postsmoothing, which provides results, which are indistinguishable from results obtained by direct discretization using cubic splines. 
G. Cannizzaro, P. Friz, P. Gassiat, Malliavin calculus for regularity structures: The case of gPAM, Journal of Functional Analysis, 272 (2017), pp. 363419, DOI 10.1016/j.jfa.2016.09.024 .
Abstract
Malliavin calculus is implemented in the context of Hairer (2014) [16]. This involves some constructions of independent interest, notably an extension of the structure which accommodates a robust, and purely deterministic, translation operator, in directions, between ?models?. In the concrete context of the generalized parabolic Anderson model in 2D ? one of the singular SPDEs discussed in the aforementioned article ? we establish existence of a density at positive times. 
J.D. Deuschel, P. Friz, M. Maurelli, M. Slowik, The enhanced Sanov theorem and propagation of chaos, Stochastic Processes and their Applications, 128 (2018), pp. 22282269 (published online on 21.09.2017), DOI 10.1016/j.spa.2017.09.010 .
Abstract
We establish a Sanov type large deviation principle for an ensemble of interacting Brownian rough paths. As application a large deviations for the (klayer, enhanced) empirical measure of weakly interacting diffusions is obtained. This in turn implies a propagation of chaos result in a space of rough paths and allows for a robust analysis of the particle system and its McKean?Vlasov type limit, as shown in two corollaries. 
L.T. Ding, P. Mathé, Minimax rates for statistical inverse problems under general source conditions, Computational Methods in Applied Mathematics, pp. published online on 05.12.2017, urlhttps://doi.org/10.1515/cmam20170055, DOI 10.1515/cmam20170055 .
Abstract
We describe the minimax reconstruction rates in linear illposed equations in Hilbert space when smoothness is given in terms of general source sets. The underlying fundamental result, the minimax rate on ellipsoids, is proved similarly to the seminal study by D. L. Donoho, R. C. Liu, and B. MacGibbon, it Minimax risk over hyperrectangles, and implications, Ann. Statist. 18, 1990. These authors highlighted the special role of the truncated series estimator, and for such estimators the risk can explicitly be given. We provide several examples, indicating results for statistical estimation in illposed problems in Hilbert space. 
A. Gasnikov, E. Gasnikova, P. Dvurechensky, A. Mohammed, E. Chernousova, About the power law of the PageRank vector component distribution. Part 1. Numerical methods for finding the PageRank vector (Original Russian text published in Sib. Zh. Vychisl. Mat., 20 (2017), pp. 359378), Numerical Analysis and Applications, 10 (2017), pp. 299312.

Y. Nesterov, V. Spokoiny, Random gradientfree minimization of convex functions, Foundations of Computational Mathematics. The Journal of the Society for the Foundations of Computational Mathematics, 17 (2017), pp. 527566.
Abstract
Summary: In this paper, we prove new complexity bounds for methods of convex optimization based only on computation of the function value. The search directions of our schemes are normally distributed random Gaussian vectors. It appears that such methods usually need at most nn times more iterations than the standard gradient methods, where nn is the dimension of the space of variables. This conclusion is true for both nonsmooth and smooth problems. For the latter class, we present also an accelerated scheme with the expected rate of convergence O(n2k2)O(n2k2), where kk is the iteration counter. For stochastic optimization, we propose a zeroorder scheme and justify its expected rate of convergence O(nk1/2)O(nk1/2). We give also some bounds for the rate of convergence of the random gradientfree methods to stationary points of nonconvex functions, for both smooth and nonsmooth cases. Our theoretical results are supported by preliminary computational experiments. 
CH. Bayer, M. Siebenmorgen, R. Tempone, Smoothing the payoff for efficient computation of basket option prices, Quantitative Finance, 18 (2018), pp. 491505 (published online on 20.07.2017), DOI 10.1080/14697688.2017.1308003 .
Abstract
We consider the problem of pricing basket options in a multivariate Black Sc holes or Variance Gamma model. From a numerical point of view, pricing such options corresponds to moderate and high dimensional numerical integration problems with non smooth integrands. Due to this lack of regularity, higher order numerical integration tech niques may not be directly available, requiring the use of methods like Monte Carlo specif ically designed to work for nonregular problems. We propose to use the inherent smooth ing property of the density of the underlying in the above models to mollify the payo ff function by means of an exact conditional expectation. The resulting conditional expec tation is unbiased and yields a smooth integrand, which is amenable to the e ffi cient use of adaptive sparse grid cubature. Numerical examples indicate that the highorder method may perform orders of magnitude faster compared to Monte Carlo or Quasi Monte Carlo in dimensions up to 25. 
CH. Bayer, U. Horst, J. Qiu, A functional limit theorem for limit order books with state dependent price dynamics, The Annals of Applied Probability, 27 (2017), pp. 27532806.
Abstract
We consider a stochastic model for the dynamics of the twosided limit order book (LOB). For the joint dynamics of best bid and ask prices and the standing buy and sell volume densities, we derive a functional limit theorem, which states that our LOB model converges to a continuoustime limit when the order arrival rates tend to infinity, the impact of an individual order arrival on the book as well as the tick size tend to zero. The limits of the standing buy and sell volume densities are described by two linear stochastic partial differential equations, which are coupled with a twodimensional reflected Brownian motion that is the limit of the best bid and ask price processes. 
P. Friz, J. Diehl, P. Gassiat, Stochastic control with rough paths, Applied Mathematics and Optimization. An International Journal with Applications to Stochastics, 75 (2017), pp. 285315, DOI 10.1007/s0024501693339 .

P. Friz, S. Gerhold, A. Pinter, Option pricing in the moderate deviations regime, Mathematical Finance. An International Journal of Mathematics, Statistics and Financial Economics, published online on 25.08.2017, urlhttps://doi.org/10.1111/mafi.12156, DOI 10.1111/mafi.12156 .

P. Friz, A. Shekhar, General rough integration, Lévy rough paths and a LévyKintchine type formula, The Annals of Probability, 45 (2017), pp. 27072765, DOI 10.1214/16 AOP1123 .

P. Friz, A. Shekhar, On the existence of SLE trace: Finite energy drivers and nonconstant $kappa$, Probability Theory and Related Fields, 169 (2017), pp. 353376.

P. Friz, H. Tran , On the regularity of SLE trace, Forum of Mathematics. Sigma, 5 (2017), pp. e19/1e19/17, DOI 10.1017/fms.2017.18 .
Abstract
We revisit regularity of SLE trace, for all , and establish Besov regularity under the usual halfspace capacity parametrization. With an embedding theorem of Garsia?Rodemich?Rumsey type, we obtain finite moments (and hence almost surely) optimal variation regularity with index , improving on previous works of Werness, and also (optimal) Hölder regularity à la Johansson Viklund and Lawler. 
P. Mathé, S.V. Pereverzev, Complexity of linear illposed problems in Hilbert space, Journal of Complexity, 38 (2017), pp. 5067.

H.J. Mucha, Assessment of stability in partitional clustering using resampling techniques, Archives of Data Science Series A, 1 (2017), pp. 2135, DOI 10.5445/KSP/1000058747/02 .

V. Spokoiny, Penalized maximum likelihood estimation and effective dimension, Annales de l'Institut Henri Poincare. Probabilites et Statistiques, 53 (2017), pp. 389429, DOI 10.1214/15AIHP720 .
Contributions to Collected Editions

Y. Bruned, I. Chevyrev, P. Friz, Examples of renormalized SDEs, in: Stochastic Partial Differential Equations and Related Fields, A. Eberle, M. Grothaus, W. Hoh, M. Kassmann, W. Stannat, G. Trutnau, eds., 229 of Springer Proceedings in Mathematics & Statistics, Springer Nature Switzerland AG, Cham, 2018, pp. 303317, DOI 10.1007/9783319749297 .

P. Dvurechensky, A. Gasnikov, A. Kroshnin, Computational optimal transport: Complexity by accelerated gradient descent is better than by Sinkhorn's algorithm, in: Proceedings of the 35th International Conference on Machine Learning, J. Dy, A. Krause, eds., 80 of Proceedings of Machine Learning Research, 2018, pp. 13671376.

TH. Koprucki, A. Maltsi, T. Niermann, T. Streckenbach, K. Tabelow, J. Polzehl, Towards modelbased geometry reconstruction of quantum dots from TEM, in: Proceedings of the 18th International Conference on Numerical Simulation of Optoelectronic DeProceediProceedings of the 18th International Conference on Numerical Simulation of Optoelectronic DeProceedings of the 18th International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD 2018)vices (NUSOD 2018)ngs of the 18th International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD 2018)vices (NUSOD 2018), A. Djurišić, J. Piprek, eds., IEEE Conference Publications Management Group, Piscataway, 2018, pp. 115116.

O. Marquardt, P. Mathé, Th. Koprucki, M. Caro, M. Willatzen, Datadriven electronic structure calculations in semiconductor nanostructures  beyond the eightband k.p formalism, in: Proceedings of the 18th International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD 2018), A. Djurišić, J. Piprek, eds., IEEE Conference Publications Management Group, 2018, pp. 5556.

P. Mathé, S. Agapiou, Posterior contraction in Bayesian inverse problems under Gaussian priors, in: New Trends in Parameter Identification for Mathematical Models, B. Hofmann, A. Leitao, J. Passamani Zubelli, eds., Trends in Mathematics, Springer, Basel, 2018, pp. 129, DOI 10.1007/9783319708249 .

H.J. Mucha, H.G. Bartel, I. Reiche, K. Müller, Multivariate statistische Auswertung von archäometrischen Messwerten von MammutElfenbein, in: Archäometrie und Denkmalpflege 2018, L. Glaser, ed., Verlag Deutsches ElektronenSynchrotron, Hamburg, 2018, pp. 162165, DOI 10.3204/DESYPROC201801 .

TH. Koprucki, M. Kohlhaase, D. Müller, K. Tabelow, Mathematical models as research data in numerical simulation of optoelectronic devices, in: Numerical Simulation of Optoelectronic Devices (NUSOD), 2017, pp. 225 226, DOI 10.1109/NUSOD.2017.8010073 .
Abstract
Mathematical models are the foundation of numerical simulation of optoelectronic devices. We present a concept for a machineactionable as well as humanunderstandable representation of the mathematical knowledge they contain and the domainspecific knowledge they are based on. We propose to use theory graphs to formalize mathematical models and model pathway diagrams to visualize them. We illustrate our approach by application to the stationary onedimensional driftdiffusion equations (van Roosbroeck system). 
M. Kohlhase, Th. Koprucki, D. Müller, K. Tabelow, Mathematical models as research data via flexiformal theory graphs, in: Intelligent Computer Mathematics: 10th International Conference, CICM 2017, Edinburgh, UK, July 1721, 2017, Proceedings, H. Geuvers, M. England, O. Hasan, F. Rabe , O. Teschke, eds., 10383 of Lecture Notes in Artificial Intelligence and Lecture Notes in Computer Science, Springer International Publishing AG, Cham, 2017, pp. 224238, DOI 10.1007/9783319620756_16 .
Abstract
Mathematical modeling and simulation (MMS) has now been established as an essential part of the scientific work in many disciplines. It is common to categorize the involved numerical data and to some extent the corresponding scientific software as research data. But both have their origin in mathematical models, therefore any holistic approach to research data in MMS should cover all three aspects: data, software, and models. While the problems of classifying, archiving and making accessible are largely solved for data and first frameworks and systems are emerging for software, the question of how to deal with mathematical models is completely open. In this paper we propose a solution  to cover all aspects of mathematical models: the underlying mathematical knowledge, the equations, boundary conditions, numeric approximations, and documents in a flexiformal framework, which has enough structure to support the various uses of models in scientific and technology workflows. Concretely we propose to use the OMDoc/MMT framework to formalize mathematical models and show the adequacy of this approach by modeling a simple, but nontrivial model: van Roosbroeck's driftdiffusion model for onedimensional devices. This formalization  and future extensions  allows us to support the modeler by e.g. flexibly composing models, visualizing Model Pathway Diagrams, and annotating model equations in documents as induced from the formalized documents by flattening. This directly solves some of the problems in treating MMS as "research data” and opens the way towards more MKM services for models. 
H.J. Mucha, T.M. Gluhak, Finding groups in compositional data  Some experiments, in: Big data clustering: Data preprocessing, variable selection, and dimension reduction, H.J. Mucha, ed., WIAS Reports, 2017, pp. 97105.
Abstract
The talk is concerned with finding groups (clusters) in compositional data, that is nonnegative data with row sums (or column sums, respectively) equal to a constant, usually 1 in case of proportions or 100 in case of percentages. Without loss of generality, the cluster analysis of observations (row points) of compositional data is considered here, where the row profiles contains parts of some whole. Special distance functions between the profiles are proposed. Finally, applications to archaeometry are presented.
Preprints, Reports, Technical Reports

H.J. Mucha, Big data clustering: Data preprocessing, variable selection, and dimension reduction, Report no. 29, WIAS, Berlin, 2017, DOI 10.20347/WIAS.REPORT.29 .
PDF (20 MByte) 
CH. Bayer, B. Stemper, Deep calibration of rough stochastic volatility models, Preprint no. 2547, WIAS, Berlin, 2018, DOI 10.20347/WIAS.PREPRINT.2547 .
Abstract, PDF (3663 kByte)
Sparked by Alòs, León und Vives (2007); Fukasawa (2011, 2017); Gatheral, Jaisson und Rosenbaum (2018), socalled rough stochastic volatility models such as the rough Bergomi model by Bayer, Friz und Gatheral (2016) constitute the latest evolution in option price modeling. Unlike standard bivariate diffusion models such as Heston (1993), these nonMarkovian models with fractional volatility drivers allow to parsimoniously recover key stylized facts of market implied volatility surfaces such as the exploding powerlaw behaviour of the atthemoney volatility skew as time to maturity goes to zero. Standard model calibration routines rely on the repetitive evaluation of the map from model parameters to BlackScholes implied volatility, rendering calibration of many (rough) stochastic volatility models prohibitively expensive since there the map can often only be approximated by costly Monte Carlo (MC) simulations (Bennedsen, Lunde & Pakkanen, 2017; McCrickerd & Pakkanen, 2018; Bayer et al., 2016; Horvath, Jacquier & Muguruza, 2017). As a remedy, we propose to combine a standard LevenbergMarquardt calibration routine with neural network regression, replacing expensive MC simulations with cheap forward runs of a neural network trained to approximate the implied volatility map. Numerical experiments confirm the high accuracy and speed of our approach. 
CH. Bayer, M. Redmann, J.G.M. Schoenmakers, Dynamic programming for optimal stopping via pseudoregression, Preprint no. 2532, WIAS, Berlin, 2018, DOI 10.20347/WIAS.PREPRINT.2532 .
Abstract, PDF (337 kByte)
We introduce new variants of classical regressionbased algorithms for optimal stopping problems based on computation of regression coefficients by Monte Carlo approximation of the corresponding L^{2} inner products instead of the leastsquares error functional. Coupled with new proposals for simulation of the underlying samples, we call the approach "pseudo regression". We show that the approach leads to asymptotically smaller errors, as well as less computational cost. The analysis is justified by numerical examples. 
D. Belomestny, J.G.M. Schoenmakers, V. Spokoiny, Y. Tavyrikov, Optimal stopping via deeply boosted backward regression, Preprint no. 2530, WIAS, Berlin, 2018, DOI 10.20347/WIAS.PREPRINT.2530 .
Abstract, PDF (209 kByte)
In this note we propose a new approach towards solving numerically optimal stopping problems via boosted regression based Monte Carlo algorithms. The main idea of the method is to boost standard linear regression algorithms in each backward induction step by adding new basis functions based on previously estimated continuation values. The proposed methodology is illustrated by several numerical examples from finance. 
E. Balteau, K. Tabelow, J. Ashburner, M.F. Callaghan, B. Draganski, G. Helms, F. Kherif, T. Leutritz, A. Lutti, Ch. Phillips, E. Reimer, L. Ruthotto, M. Seif, N. Weiskopf, G. Ziegler, S. Mohammadi, hMRI  A toolbox for using quantitative MRI in neuroscience and clinical research, Preprint no. 2527, WIAS, Berlin, 2018, DOI 10.20347/WIAS.PREPRINT.2527 .
Abstract, PDF (3102 kByte)
Quantitative magnetic resonance imaging (qMRI) finds increasing application in neuroscience and clinical research due to its sensitivity to microstructural properties of brain tissue, e.g. axon, myelin, iron and water concentration. We introduce the hMRItoolbox, an easytouse opensource tool for handling and processing of qMRI data presented together with an example dataset. This toolbox allows the estimation of highquality multiparameter qMRI maps (longitudinal and effective transverse relaxation rates R1 and R2*, proton density PD and magnetisation transfer MT) that can be used for calculation of standard and novel MRI biomarkers of tissue microstructure as well as improved delineation of subcortical brain structures. Embedded in the Statistical Parametric Mapping (SPM) framework, it can be readily combined with existing SPM tools for estimating diffusion MRI parameter maps and benefits from the extensive range of available tools for highaccuracy spatial registration and statistical inference. As such the hMRItoolbox provides an efficient, robust and simple framework for using qMRI data in neuroscience and clinical research. 
J. Polzehl, K. Papafitsoros, K. Tabelow, Patchwise adaptive weights smoothing, Preprint no. 2520, WIAS, Berlin, 2018, DOI 10.20347/WIAS.PREPRINT.2520 .
Abstract, PDF (28 MByte)
Image reconstruction from noisy data has a long history of methodological development and is based on a variety of ideas. In this paper we introduce a new method called patchwise adaptive smoothing, that extends the PropagationSeparation approach by using comparisons of local patches of image intensities to define local adaptive weighting schemes for an improved balance of reduced variability and bias in the reconstruction result. We present the implementation of the new method in an R package aws and demonstrate its properties on a number of examples in comparison with other stateofthe art image reconstruction methods. 
CH. Bayer, D. Belomestny, M. Redmann, S. Riedel, J.G.M. Schoenmakers, Solving linear parabolic rough partial differential equations, Preprint no. 2506, WIAS, Berlin, 2018, DOI 10.20347/WIAS.PREPRINT.2506 .
Abstract, PDF (4827 kByte)
We study linear rough partial differential equations in the setting of [Friz and Hairer, Springer, 2014, Chapter 12]. More precisely, we consider a linear parabolic partial differential equation driven by a deterministic rough path W of Hölder regularity α with ⅓ < α ≤ ½ . Based on a stochastic representation of the solution of the rough partial differential equation, we propose a regression Monte Carlo algorithm for spatiotemporal approximation of the solution. We provide a full convergence analysis of the proposed approximation method which essentially relies on the new bounds for the higher order derivatives of the solution in space. Finally, a comprehensive simulation study showing the applicability of the proposed algorithm is presented. 
L. Antoine, P. Pigato, Maximum likelihood drift estimation for a threshold diffusion, Preprint no. 2497, WIAS, Berlin, 2018, DOI 10.20347/WIAS.PREPRINT.2497 .
Abstract, PDF (425 kByte)
We study the maximum likelihood estimator of the drift parameters of a stochastic differential equation, with both drift and diffusion coefficients constant on the positive and negative axis, yet discontinuous at zero. This threshold diffusion is called the drifted Oscillating Brownian motion. The asymptotic behaviors of the positive and negative occupation times rule the ones of the estimators. Differently from most known results in the literature, we do not restrict ourselves to the ergodic framework: indeed, depending on the signs of the drift, the process may be ergodic, transient or null recurrent. For each regime, we establish whether or not the estimators are consistent; if they are, we prove the convergence in long time of the properly rescaled difference of the estimators towards a normal or mixed normal distribution. These theoretical results are backed by numerical simulations. 
P. Pigato, Extreme atthemoney skew in a local volatility model, Preprint no. 2468, WIAS, Berlin, 2017.
Abstract, PDF (270 kByte)
We consider a local volatility model, with volatility taking two possible values, depending on the value of the underlying with respect to a fixed threshold. When the threshold is taken atthemoney, we establish exact pricing formulas and compute shorttime asymptotics of the implied volatility surface. We derive an exact formula for the atthemoney implied volatility skew, which explodes as T^{1/2}, reproducing the empirical "steep short end of the smile". This behavior does not depend on the precise choice of the parameters, but simply follows from the "regimeswitch" of the local volatility atthemoney. 
A. Lejay, P. Pigato, A threshold model for local volatility: Evidence of leverage and mean reversion effects on historical data, Preprint no. 2467, WIAS, Berlin, 2017, DOI 10.20347/WIAS.PREPRINT.2467 .
Abstract, PDF (534 kByte)
In financial markets, low prices are generally associated with high volatilities and viceversa, this well known stylized fact usually being referred to as leverage effect. We propose a local volatility model, given by a stochastic differential equation with piecewise constant coefficients, which accounts of leverage and meanreversion effects in the dynamics of the prices. This model exhibits a regime switch in the dynamics accordingly to a certain threshold. It can be seen as a continuous time version of the SelfExciting Threshold Autoregressive (SETAR) model. We propose an estimation procedure for the volatility and drift coefficients as well as for the threshold level. Tests are performed on the daily prices of 21 assets. They show empirical evidence for leverage and meanreversion effects, consistent with the results in the literature. 
D. Belomestny, J.G.M. Schoenmakers, Regression on particle systems connected to meanfield SDEs with applications, Preprint no. 2464, WIAS, Berlin, 2017, DOI 10.20347/WIAS.PREPRINT.2464 .
Abstract, PDF (268 kByte)
In this note we consider the problem of using regression on interacting particles to compute conditional expectations for McKeanVlasov SDEs. We prove general result on convergence of linear regression algorithms and establish the corresponding rates of convergence. Application to optimal stopping and variance reduction are considered. 
P. Goyal, M. Redmann, Towards timelimited $H_2$optimal model order reduction, Preprint no. 2441, WIAS, Berlin, 2017, DOI 10.20347/WIAS.PREPRINT.2441 .
Abstract, PDF (363 kByte)
In order to solve partial differential equations numerically and accurately, a high order spatial discretization is usually needed. Model order reduction (MOR) techniques are often used to reduce the order of spatiallydiscretized systems and hence reduce computational complexity. A particular class of MOR techniques are H_2optimal methods such as the iterative rational Krylov subspace algorithm (IRKA) and related schemes. However, these methods are used to obtain good approximations on a infinite timehorizon. Thus, in this work, our main goal is to discuss MOR schemes for timelimited linear systems. For this, we propose an alternative timelimited H_2norm and show its connection with the timelimited Gramians. We then provide firstorder optimality conditions for an optimal reduced order model (ROM) with respect to the timelimited H_2norm. Based on these optimality conditions, we propose an iterative scheme which upon convergences aims at satisfying these conditions. Then, we analyze how far away the obtained ROM is from satisfying the optimality conditions. We test the efficiency of the proposed iterative scheme using various numerical examples and illustrate that the newly proposed iterative method can lead to a better reducedorder compared to unrestricted IRKA in the time interval of interest. 
S. Mohammadi, Ch. D'alonzo, L. Ruthotto, J. Polzehl, I. Ellerbrock, M.F. Callaghan, N. Weiskopf, K. Tabelow, Simultaneous adaptive smoothing of relaxometry and quantitative magnetization transfer mapping, Preprint no. 2432, WIAS, Berlin, 2017, DOI 10.20347/WIAS.PREPRINT.2432 .
Abstract, PDF (3888 kByte)
Attempts for invivo histology require a high spatial resolution that comes with the price of a decreased signaltonoise ratio. We present a novel iterative and multiscale smoothing method for quantitative Magnetic Resonance Imaging (MRI) data that yield proton density, apparent transverse and longitudinal relaxation, and magnetization transfer maps. The method is based on the propagationseparation approach. The adaptivity of the procedure avoids the inherent bias from blurring subtle features in the calculated maps that is common for nonadaptive smoothing approaches. The characteristics of the methods were evaluated on a highresolution data set (500 μ isotropic) from a single subject and quantified on data from a multisubject study. The results show that the adaptive method is able to increase the signaltonoise ratio in the calculated quantitative maps while largely avoiding the bias that is otherwise introduced by spatially blurring values across tissue borders. As a consequence, it preserves the intensity contrast between white and gray matter and the thin cortical ribbon. 
V. Avanesov, N. Buzun, Changepoint detection in highdimensional covariance structure, Preprint no. 2404, WIAS, Berlin, 2017.
Abstract, PDF (383 kByte)
In this paper we introduce a novel approach for an important problem of break detection. Specifically, we are interested in detection of an abrupt change in the covariance structure of a highdimensional random process ? a problem, which has applications in many areas e.g., neuroimaging and finance. The developed approach is essentially a testing procedure involving a choice of a critical level. To that end a nonstandard bootstrap scheme is proposed and theoretically justified under mild assumptions. Theoretical study features a result providing guaranties for break detection. All the theoretical results are established in a highdimensional setting (dimensionality p n). Multiscale nature of the approach allows for a tradeoff between sensitivity of break detection and localization. The approach can be naturally employed in an online setting. Simulation study demonstrates that the approach matches the nominal level of false alarm probability and exhibits high power, outperforming a recent approach.
Talks, Poster

A. Maltsi, Th. Koprucki, T. Niermann, T. Streckenbach, K. Tabelow, J. Polzehl, Computing TEM images of semiconductor nanostructures, Applied Mathematics and Simulation for Semiconductors (AMaSiS 2018), WIAS Berlin, October 8  10, 2018.

A. Suvorikova, CLT for barycenters in 2Wasserstein space, Mass TransportiationTheory: Opening perspectives in Statistics, Probability and Computer Science, June 3  10, 2018, Universidad de Valladolid, Departamento de Estadística e Investigación Operativa, Spain, June 5, 2018.

A. Suvorikova, Central limit theorem for Wasserstein barycenters of Gaussian measures, 4th Conference of the International Society for Nonparametric Statistics, June 11  15, 2018, University of Salerno, Italy, June 15, 2018.

A. Suvorikova, Central limit theorem for barycenters in 2Wasserstein space, RuhrUniversität Bochum, May 30, 2018.

A. Suvorikova, Construction of nonasymptotic confidence sets in 2Wasserstein space, Structural Learning Seminar, Skolkovo Institute of Science and Technology, Moscow, Russian Federation, May 10, 2018.

A. Suvorikova, Gaussian process forecast with multidimensional distributional input, Haindorf Seminar 2018, January 23  27, 2018, HumboldtUniversität zu Berlin, Wirtschaftswissenschaftliche Fakultät, Hejnice, Czech Republic, January 25, 2018.

A. Suvorikova, Statistical inference with optimal transport, Spring School ``Structural Inference 2018'' and Closing Workshop, March 4  9, 2018.

A. Suvorikova, Statistical inference with optimal transport, WIASDay, February 22  23, 2018.

N. Buzun, Sein's method, Haindorf Seminar 2018, January 24  27, 2018, HumboldtUniversität zu Berlin, Wirtschaftswissenschaftliche Fakultät, Hejnice, Czech Republic, January 26, 2018.

M. Coghi, Mean field limit of interacting filaments for 3D Euler equations, BielefeldEdinburghSwansea Stochastic Spring, March 26  28, 2018, Universität Bielefeld, Fakultät für Mathematik, March 27, 2018.

M. Coghi, Mean field limit of interacting filaments for 3D Euler equations, BSDEs, Information and McKeanVlasov equations, September 10  12, 2018, University of Leeds, School of Mathematics, UK, September 10, 2018.

M. Coghi, Nonlocal stochastic scalar conservation laws, Bielefeld Stochastic Afternoon, Universität Bielefeld, Fakultät für Mathematik, October 16, 2018.

M. Coghi, Pathwise McKeanVlasov theory, 10th OxfordBerlin Young Researchers Meeting on Applied Stochastic Analysis, November 29  December 1, 2018, University of Oxford, Mathematical Institute, UK, December 1, 2018.

M. Maurelli, McKeanVlasov SDEs with irregular drift: Large deviations for particle approximation, University of Oxford, Mathematical Institute, UK, March 5, 2018.

M. Maurelli, Sanov theorem for Brownian rough paths and an application to interacting particles, Università di Roma La Sapienza, Dipartimento di Matematica Guido Castelnuovo, Italy, January 18, 2018.

M. Maurelli , A McKeanVlasov SDE with reflecting boundaries, CASA Colloquium, Eindhoven University of Technology, Department of Mathematics and Computer Science, Netherlands, January 10, 2018.

P. Pigato, Asymptotic analysis of rough volatility models, Probability Seminar, L'Università di MilanoBicocca, Dipartimento di Matematica e Applicazioni, Italy, July 13, 2018.

P. Pigato, Asymptotic analysis of rough volatility models, Seminar of the Research Training Group 2131, RuhrUniversität Bochum, Research Training Group 2131, June 25, 2018.

P. Pigato, Density and tube estimates for diffusion processes under Hormandertype conditions, Séminaire (de calcul) stochastique, Université de Strasbourg, Institut de recherche mathématique avancée), France, November 23, 2018.

P. Pigato, Estimation of piecewiseconstant coefficients in a stochastic differential equation, The 40th Conference on Stochastic Processes and their Applications  SPA 2018, University of Gothenburg, Göteborg, Sweden, June 13, 2018.

P. Pigato, Faits stilisés et modelisation de la volatilité, École Polytechnique, Palaiseau, France, April 20, 2018.

P. Pigato, Faits stilisés et modelisation de la volatilitë, Seminaire, Institut de Science Financière et d'Assurances  Université Lyon 1, France, May 14, 2018.

P. Pigato, Precise asymptotics of rough stochastic volatility models, University of Trento, Department of Mathematics, November 16, 2018.

P. Pigato, Short dated option pricing under rough volatility, BerlinParis Young Researchers Workshop Stochastic Analysis with applications in Biology and Finance, May 2  4, 2018, Institut des Systèmes Complexes de Paris IledeFrance (ISCPIF), National Center for Scientific Research, Paris, France, May 4, 2018.

M. Redmann, Beyond the theory of ordinary differential equations, Seminar of the Deparment of Mathematics and Computer Science, University of Southern Denmark, Department of Mathematics and Computer Science, Odense, Denmark, February 22, 2018.

M. Redmann, Beyond the theory of ordinary differential equations, Institute's Seminar, February 22, 2018, University of Southern Denmark, Department of Mathematics and Computer Science (IMADA), Odense, Denmark, February 22, 2018.

M. Redmann, Numerical approximations of parabolic rough PDEs, Harmonic Analysis for Stochastic PDEs, July 10  13, 2018, Delft University of Technology, Netherlands, July 10, 2018.

M. Redmann, Solving linear parabolic rough partial differential equations, 13th German Probability and Statistics Days 2018, February 27  March 2, 2018, AlbertLudwigsUniversität Freiburg, Abteilung für Mathematische Stochastik, March 1, 2018.

M. Redmann, Solving parabolic rough partial differential equations using regression, 13th International Conference in Monte Carlo & QuasiMonte Carlo Methods in Scientific Computing, July 1  6, 2018, University of Rennes, Faculty of Economics, France, July 5, 2018.

M. Redmann, Solving stochastic partial differential equations, Universität Greifswald, Institut für Mathematik und Informatik, April 19, 2018.

B. Stemper, Calibration of the rough Bergomi model via neural networks, 9th Annual ERC BerlinOxford Young Researchers Meeting on Applied Stochastic Analysis, June 14  16, 2018, WIAS Berlin, June 14, 2018.

B. Stemper, Pricing under rough volatility, Premia Meeting, Centre de recherche INRIA Paris, France, March 12, 2018.

B. Stemper, Shorttime nearthemoney skew in rough volatility models, 10th World Congress of the Bachelier Finance Society, July 16  20, 2018, Bachelier Finance Society, Dublin, Ireland, July 19, 2018.

CH. Bayer, Shortime nearthemoney skew in rough fractional volatility models, 9th International Workshop on Applied Probability, June 18  21, 2018, Eörvös Loránd University (ELU), Budapest, Hungary, June 19, 2018.

CH. Bayer, Smoothing the payoff for computation of basket options, BerlinParis Young Researchers Workshop Stochastic Analysis with applications in Biology and Finance, May 2  4, 2018, Institut des Systèmes Complexes de Paris IledeFrance (ISCPIF), National Center for Scientific Research, Paris, France, May 3, 2018.

CH. Bayer, Smoothing the payoff for computation of basket options, 13th International Conference in Monte Carlo & QuasiMonte Carlo Methods in Scientific Computing, July 1  6, 2018, University of Rennes, Faculty of Economics, France, July 3, 2018.

CH. Bayer, Smoothing the payoff for computation of basket options, Stochastic Methods in Finance and Physics, July 23  27, 2018, National Technical University of Athens, Department of Mathematics, Heraklion, Greece.

P. Dvurechennky, Universal method for variational inequalities with Holdercontinuous monotone operator, Mathematics and Image Analysis, January 15  17, 2018.

P. Dvurechensky, Computational optimal transport: Accelerated gradient descent vs Sinkhorn, ISMP 2018 Bordeaux, July 1  6, 2018, University of Bordeaux, Institut de Mathématiques, France, July 4, 2018.

P. Dvurechensky, Computational optimal transport: Accelerated gradient descent vs Sinkhorn's algorithm, Statistical Optimal Transport, July 24  25, 2018, Skolkovo Institute of Science and Technology, Moskau, Russian Federation, July 25, 2018.

P. Dvurechensky, Computational optimal transport: Complexity by accelerated gradient descent is better than by Sinkhorn's algorithm, The 35th International Conference on Machine Learning (ICML 2018), Stockholm, Sweden, July 9  15, 2018.

P. Dvurechensky, Computational optimal transport: Complexity by accelerated gradient descent is better than by Sinkhorn's algorithm, The 35th International Conference on Machine Learning (ICML 2018), July 9  15, 2018, International Machine Learning Society (IMLS), Stockholm, Sweden, July 11, 2018.

P. Dvurechensky, Decentralize and randomize: Faster algorithm for Wasserstein barycenters, Thirtysecond Conference on Neural Information Processing Systems, Montréal, Canada, December 3  8, 2018.

P. Dvurechensky, Faster algorithms for (regularized) optimal transport, Grenoble Optimization Days 2018, June 28  29, 2018, Université Grenoble Alpes, Laboratoire Jean Kuntzmann, France, June 29, 2018.

P. Dvurechensky, Faster algorithms for (regularized) optimal transport, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow region, Russian Federation, March 30, 2018.

P. Dvurechensky, Primaldual methods for solving infinitedimensional games, Games, Dynamics and Optimization GDO2018, March 13  15, 2018, Universität Wien, Fakultät für Mathematik, Austria, March 15, 2018.

P. Dvurechensky, Statistical optimal transport, WIASDay, February 23, 2018.

P. Friz, Analysis of rough volatility via rough paths / regularity structures, METE  Mathematics and Economics: Trends and Explorations. A conference celebrating Mete Soner's 60th birthday and his contributions to Analysis, Control, Finance and Probability, June 4  8, 2018, Eidgenössische Technische Hochschule Zürich, Forschungsinstitut für Mathematik, Switzerland, June 5, 2018.

P. Friz, From rough paths and regularity structures to short dated option pricing under rough volatility, Workshop on Mathematical Finance and related Issues, Osaka University, Nakanoshima Center, Japan, March 15, 2018.

P. Friz, Rough path analysis of rough volatility, 9th International Workshop on Applied Probability, June 18  22, 2018, Eörvös Loránd University (ELU), Budapest, Hungary, June 18, 2018.

P. Friz, Rough path analysis of rough volatility, Stochastic Analysis Seminar, Imperial College London, Department of Mathematics, Stochastic Analysis Group, UK, February 13, 2018.

P. Friz, Rough paths, stochastics and PDE's, ECMath Colloquium, July 6, 2018, HumboldtUniversität zu Berlin, July 6, 2018.

P. Friz, Stepping stoch vol and related topics, 25th Global Derivatives Trading & Risk Management 2018, Volatility modelling & Trading, May 14  18, 2018, Lissabon, Portugal, May 16, 2018.

P. Friz, Varieties of signature tensors, Workshop on Stochastic Analysis, Geometry and Statistics, June 21  22, 2018, Imperial College London, UK, June 22, 2018.

A. Koziuk, Gaussian comparison on a family of Eucledian balls, Haindorf Seminar 2018, January 23  27, 2018, HumboldtUniversität zu Berlin, Wirtschaftswissenschaftliche Fakultät, Hejnice, Czech Republic, January 26, 2018.

P. Mathé, Bayesian inverse problems with noncommuting operators, Chemnitz September of Applied Mathematics 2018, Chemnitz Symposium on Inverse Problems, September 27  28, 2018, Technische Universität Chemnitz, Fakultät für Mathematik, September 28, 2018.

P. Mathé, Relating posterior contraction for direct and inverse Bayesian problems by the modulus of continuity, International Conference on Inverse Problems, October 12  14, 2018, Fudan University, School of Mathematical Sciences, Shanghai, China, October 13, 2018.

P. Mathé, Titel: Complexity of linear illposed problems in Hilbert space, Stochastisches Kolloquium, GeorgAugustUniversität Göttingen, Institut für Mathematische Stochastik, February 7, 2018.

H.J. Mucha, Multivariate statistische Auswertung von archäometrischen Messwerten von MammutElfenbein, Tagung Archäometrie und Denkmalpflege 2018, Deutsches ElektronenSynchrotron DESY, Hamburg, March 20  24, 2018.

J. Polzehl, High resolution magnetic resonance imaging experiments  lessons in nonlinear statistical modeling, 3rd Leibniz MMS Days, February 28  March 2, 2018, "Wissenschaftszentrum Leipzig", March 1, 2018.

J. Polzehl, Modeling high dimensional data, Leibniz MMS Summer School 2018, September 3  7, 2018, Leibniz MMS Network, Oberwolfach.

J. Polzehl, Towards invivo histology of the brain  some statistical contributions, CMRR Seminar, University of Minnesota, Center for Magnetic Resonance Research (CMRR), Minneapolis, USA, October 15, 2018.

V. Spokoiny, Adaptive nonparametric clustering, Multiscale Problems in Materials Science and Biology: Analysis and Computation, January 8  12, 2018, Tsinghua University, Yau Mathematical Sciences Center, Sanya, Hainan, China, January 10, 2018.

V. Spokoiny, Bootstrap confidence sets for spectral projectors of sample covariance, 12th International Vilnius Conference on Probability Theory and Mathematical Statistics and 2018 IMS Annual Meeting on Probability and Statistics, July 2  6, 2018, Vilnius University, Lithuanian Mathematical Society and the Institute of Mathematical Statistics, Lithuania, July 5, 2018.

V. Spokoiny, Gaussian process forecast with multidimensional distributional input, 4th Conference of the International Society for Nonparametric Statistics, June 11  15, 2018, University of Salerno, Italy, June 15, 2018.

V. Spokoiny, Inference for spectral projectors, Statistical Inference for Structured Highdimensional Models, March 11  16, 2018, Mathematisches Forschungsinstitut Oberwolfach, March 14, 2018.

V. Spokoiny, Large ball probability with applications in statistics, Mathematical Workshop of the School of Applied Mathematics and Computer Science, Moscow Institute of Physics and Technology, Dolgoprudny, Russian Federation, November 30, 2018.

V. Spokoiny, Large ball probability with applications to statistical inference, 6th Princeton Day of Statistics, November 9, 2018, Princeton University, Department of Operations Research and Financial Engineering, USA, November 9, 2018.

V. Spokoiny, Manifold learning: Theory and applications, Pennsylvania State University, Department of Mathematics, State College, USA, November 14, 2018.

V. Spokoiny, Prior impact in Bayesian inference, Internatioal Seminar & Workshop:``Stochastic dynamics on large networks: Prediction and inference", October 15  16, 2018, MaxPlanckInstitut für Physik komplexer Systeme, Dresden, October 15, 2018.

V. Spokoiny, Structured nonparametric and highdimensional statistics, 2018 Joint Meeting of the Korean Mathematical Society and the German Mathematical Society, October 3  6, 2018, Korean Mathematical Society, Seoul, Korea (Republic of), October 5, 2018.

K. Tabelow, Structural adaptation for noise reduction in magnetic resonance imaging, SIAM Conference on Imaging Science, Minisymposium MS5 ``Learning and Adaptive Approaches in Image Processing'', June 5  8, 2018, Bologna, Italy, June 5, 2018.

A. Suvorikova, Construction of confidence sets in 2Wasserstein space, Haindorf Seminar 2017, January 24  28, 2017, HumboldtUniversität zu Berlin, Wirtschaftswissenschaftliche Fakultät, Hejnice, Czech Republic, January 26, 2017.

A. Suvorikova, Construction of confidence sets in 2Wasserstein space, Machine Learning Seminar, Université PaulSabatier, Institut de Mathématiques de Toulouse, France, December 1, 2017.

A. Suvorikova, Construction of nonasymptotic confidence sets in 2Wasserstein space, Spring School ``Structural Inference'' 2017, Bad Malente, March 5  10, 2017.

N. Buzun, Bootstrap for multiple hypothesis testing, Spring School ``Structural Inference'' 2017, March 5  10, 2017, DFG Research Unit FOR 1735 ``Structural Inference in Statistic'', Bad Malente, March 6, 2017.

W. Dreyer, J. Fuhrmann, P. Gajewski, C. Guhlke, M. Landstorfer, M. Maurelli, R. Müller, Stochastic model for LiFePO4electrodes, ModVal14  14th Symposium on Fuel Cell and Battery Modeling and Experimental Validation, Karlsruhe, March 2  3, 2017.

M. Maurelli, A McKeanVlasov SDE with reflecting boundaries, 8th OxfordBerlin Young Researchers Meeting on Applied Stochastic Analysis, December 14  16, 2017, University of Oxford, Mathematical Institute, UK, December 15, 2017.

M. Maurelli, Regularization by noise for scalar conservation laws, Stochastic Analysis Day, February 27, 2017, Università di Pisa, Dipartimento di Matematica, Italy, February 27, 2017.

M. Maurelli, Regularization by noise for scalar conservation laws, BerlinLeipzig Workshop in Analysis and Stochastics, November 29  December 1, 2017, MaxPlanckInstitut für Mathematik in den Naturwissenschaften, Leipzig, November 30, 2017.

M. Maurelli, Regularization by noise for scalar conservation laws, Séminaire de Probabilité et Statistique, Université de Nice SophiaAntipolis, Laboratoire Jean Alexandre Dieudonné, France, September 26, 2017.

M. Maurelli, Stochastic 2D Euler equations with transport noise, Chalmers University of Technology, Department of Mathematical Sciences, Gothenburg, Sweden, November 28, 2017.

M. Maurelli , A McKeanVlasov SDE with reflecting boundaries, Seminar of SPASS  Probability, Stochastic Analysis and Statistics in Pisa, Università di Pisa, Dipartimento di Matematica, Italy, December 18, 2017.

P. Pigato, Estimation of the parameters of a diffusion with discontinuous coefficients, 7th Annual ERC BerlinOxford Young Researchers Meeting on Applied Stochastic Analysis, May 18  20, 2017, WIASBerlin, May 20, 2017.

P. Pigato, The oscillating Brownian motion: Estimation and application to volatility modeling, Probability Seminar, Università degli Studi di Padova, Dipartimento di Matematica Pura ed Applicata, Italy, September 26, 2017.

P. Pigato, The oscillating Brownian motion: Estimation and application to volatility modelling, Finance and Stochastics Seminar, Imperial College London, Department of Mathematics, UK, November 15, 2017.

M. Redmann, A regression method to solve parabolic rough PDEs, 7th Annual ERC BerlinOxford Young Researchers Meeting on Applied Stochastic Analysis, May 18  20, 2017, WIASBerlin, May 20, 2017.

M. Redmann, A regression method to solve parabolic rough PDEs, Ninth Workshop on Random Dynamical Systems, June 14  17, 2017, Universität Bielefeld, Fakultät für Mathematik, June 15, 2017.

M. Redmann, Type II singular perturbation approximation for linear systems with Levy noise, London Mathematical Society  EPSRC Durham Symposium: Model Order Reduction, Durham University, Department of Mathematical Sciences, UK, August 14, 2017.

B. Stemper, Pricing in rough fractional volatility models via regularity structures, 3rd BerlinPrincetonSingapore Workshop on Quantitative Finance, April 19  22, 2017, HumboldtUniversität zu Berlin, April 20, 2018.

CH. Bayer, A regularity structure for rough volatility, Quantitative Finance Conference in honour of Jim Gatheral's 60th Birthday, October 13  15, 2017, New York University, Courant Institute, USA, October 14, 2017.

CH. Bayer, Numerics for rough volatility models, Ninth Workshop on Random Dynamical Systems, June 14  17, 2017, University of Bielefeld, Department of Mathematics, June 14, 2017.

CH. Bayer, Rough volatility models in finance, 19th International Congress of the ÖMG and Annual DMV Meeting, 6th Austrian Stochastics Days, September 11  15, 2017, Austrian Mathematical Society (ÖMG) and Deutsche MathematikerVereinigung (DMV), ParisLodron University of Salzburg, Austria, September 13, 2017.

CH. Bayer, Rough volatility models in finance, AMCS Seminar, King Abdullah University of Science and Technology (KAUST), Computer, Electrical and Mathematical Sciences & Engineering Division, Thuwal, Saudi Arabia, October 25, 2017.

CH. Bayer, Smoothing the payoff for efficient computation of basket option, Financial Math Seminar, Princeton University, Operations Research & Financial Engineering, USA, October 11, 2017.

CH. Bayer, Smoothing the payoff for efficient computation of basket option prices, Workshop ``Mathematics of Quantitative Finance'', February 26  March 4, 2017, Mathematisches Forschungsinstitut Oberwolfach, February 27, 2017.

CH. Bayer, Smoothing the payoff for efficient computation of basket options, Workshop on Recent Developments in Numerical Methods with Applications in Statistics and Finance, June 8  9, 2017, University of Mannheim, Graduate School of Economics and Social Sciences, June 9, 2017.

CH. Bayer, Smoothing the payoff for efficient computation of basket options, Conference on Mathematical Modelling in Finance 2017, August 30  September 2, 2017, Imperial College London, UK, September 2, 2017.

P. Dvurechensky, A unified view on accelerated randomized optimization methods: Coordinate descent, directional search, derivativefree method, Foundations of Computational Mathematics (FoCM 2017), Barcelona, Spain, July 17  19, 2017.

P. Dvurechensky, Adaptive similar triangles method: A stable alternative to Sinkhorn's algorithm for regularized optimal transport, CoEvolution of Nature and Society Modelling, Problems & Experience. Devoted to Academician Nikita Moiseev centenary (Moiseev100)., November 7  10, 2017, Russian Academy of Science, Federal Research Center Computer Science and Control, Moskau, Russian Federation, November 9, 2017.

P. Dvurechensky, Faster algorithms for optimal transport, 3. International Matheon Conference on Compressed Sensing and its Applications 2017, Berlin, December 4  8, 2017.

P. Dvurechensky, Gradient method with inexact oracle for composite nonconvex optimization, Optimization and Statistical Learning, Les Houches, France, April 10  14, 2017.

P. Dvurechensky, Gradient method with inexact oracle for composite nonconvex optimization, Foundations of Computational Mathematics (FoCM 2017), Barcelona, Spain, July 17  19, 2017.

P. Dvurechensky, Gradient method with inexact oracle for composite nonconvex optimization, 18th French  German  Italian Conference on Optimization, September 25  28, 2017, Universität Paderborn, Fakultät für Elektrotechnik, Informatik und Mathematik, Paderborn, September 27, 2017.

P. Dvurechensky, Gradient method with inexact oracle for non convex optimization, 3rd Applied Mathematics Symposium Münster: Shape, Imaging and Optimization, February 28  March 3, 2017.

P. Friz, A regularity structure for rough volatility, Global Derivates Trading & Risk Management Conference 2017, May 8  12, 2017, Barcelona, Spain, May 10, 2017.

P. Friz, An application of regularity structures to the analysis of rough volatility, Fractional Brownian Motion and Rough Models, June 8  9, 2017, Barcelona Graduate School of Economics, Spain, June 9, 2017.

P. Friz, Aspects of rough volatility, The 5th Imperial  ETH Workshop on Mathematical Finance, March 27  29, 2017, Imperial College London, UK, March 27, 2017.

P. Friz, General semimartingales and rough paths, Durham Symposium on Stochastic Analysis, July 10  20, 2017, Durham University, Department of Mathematical Sciences, UK, July 13, 2017.

P. Friz, Geometric aspects in pathwise stochastic analysis, High Risk High Gain  Groundbreaking Research in Berlin, August 31  September 3, 2017, Technische Universität Berlin, Stabsstelle Presse, September 2, 2017.

P. Friz, Multiscale systems, homogenization and rough paths, BerlinLeipzig Workshop in Analysis and Stochastics, November 29  December 1, 2017, MaxPlanckInstitut für Mathematik in den Naturwissenschaften, Leipzig, November 29, 2017.

P. Friz, Rough differential equations with jumps and their applications, JapaneseGerman Open Conference on Stochastic Analysis 2017, September 4  8, 2017, Technische Universität Kaiserslautern, Fachbereich Mathematik, September 5, 2017.

A. Koziuk, Bootstrap for the regression problem with instrumental variables, Haindorf Seminar 2017, January 24  28, 2017, HumboldtUniversität zu Berlin, Wirtschaftswissenschaftliche Fakultät, Hejnice, Czech Republic, January 26, 2017.

P. Mathé, Bayesian inverse problems with noncommuting operators, Statistical Foundations of Uncertainty Quantification for Inverse Problems Workshop, June 19  22, 2017, University of Cambridge, Center for Mathematical Sciences, UK, June 21, 2017.

P. Mathé, Complexity of supervised learning, ibcparis2017 : Information Based Complexity, HighDimensional Problems, March 14  15, 2017, Institut Henri Poincaré, Paris, France, March 15, 2017.

P. Mathé, Numerical integration (mini course), November 20  December 4, 2017, Fudan University, School of Mathematical Sciences, China.

P. Mathé, Tikhonov regularization with oversmoothing penalty, 7th GermanPolish Conference on Optimization (GPCO 2017), August 27  September 1, 2017, Mathematical Research and Conference Center of IMPAN, Będlewo, Poland, August 28, 2017.

H.J. Mucha, About the influence of multiple points of bootstrapping on the validation of clustering results, European Classification and Data Analysis Conference 2017 (ECDA2017), September 27  29, 2017, Wrocław University of Economics, Poland, September 27, 2017.

H.J. Mucha, Big data clustering: Comparison of the performance of a new fast preclustering and subsampling, German Polish Seminar on Data Analysis and Applications 2017, September 25  26, 2017, Wrocław University of Economics, Poland, September 26, 2017.

H.J. Mucha, Hierarchical clustering of big data using a special bootstrapping version, AG DANK Herbsttagung, November 17  18, 2017, Gesellschaft für KlassifikationGesellschaft für Klassifikation, Data Science Society, November 18, 2017.

J. Polzehl, Connectivity networks in neuroscience  Construction and analysis, Summer School 2017: Probabilistic and Statistical Methods for Networks, August 21  September 1, 2017, Technische Universität Berlin, Berlin Mathematical School.

J. Polzehl, Neue statistische Methoden zur Biomarkerselektion, Symposium ``Biomarker: Objektive Parameter als Grundlage für die erfolgreiche individuelle Therapie'', November 21, 2017, Leibniz Gesundheitstechnologien, Berlin, November 21, 2017.

J. Polzehl, Structural adaptation  A statistical concept for image denoising, Seminar, Isaac Newton Institute, Programme ``Variational Methods and Effective Algorithms for Imaging and Vision'', Cambridge, UK, December 5, 2017.

J. Polzehl, Toward invivo histology of the brain, NeuroStatistics: The Interface between Statistics and Neuroscience, University of Minnesota, School of Statistics (IRSA), Minneapolis, USA, May 5, 2017.

J. Polzehl, Towards invivo histology of the brain, Berlin Symposium 2017: Modern Statistical Methods From Data to Knowledge, December 14  15, 2017, organized by Indiana Laboratory of Biostatistical Analysis of Large Data with Structure (ILBALDS), Berlin, December 14, 2017.

J.G.M. Schoenmakers, Optimal stopping and control via approximative dynamic programming, Workshop on Mathematics of Deep Learning 2017, September 13  15, 2017, WIAS, Berlin, September 13, 2017.

J.G.M. Schoenmakers, Projected particle methods for solving McKeanVlasov SDEs, Dynstoch 2017, April 5  7, 2017, Universität Siegen, Department Mathematik, April 6, 2017.

J.G.M. Schoenmakers, Projective simulation and regression methods for MckeanVlasov SDE systems, Mathematisches Kolloquium, Universität DuisburgEssen, Fakultät für Mathematik, November 29, 2017.

V. Spokoiny, Adaptive clustering and network clustering, 60th MIPT Scientific Conference, Moscow State University, Moscow Institute of Physics and Technology, Russian Federation, November 25, 2017.

V. Spokoiny, Adaptive nonparametric clustering, Optimization and Statistical Learning, Les Houches, France, April 10  14, 2017.

V. Spokoiny, Adaptive nonparametric clustering, Workshop ``Statistical Recovery of Discrete, Geometric and Invariant Structures'', March 19  25, 2017, Mathematisches Forschungsinstitut Oberwolfach, March 24, 2017.

V. Spokoiny, Adaptive nonparametric clustering, Rencontres de Statistique Mathématique, December 18  22, 2017, Centre International de Rencontres Mathématiques (CIRM), Luminy, France, December 18, 2017.

V. Spokoiny, Bootstrap confidence sets for spectral projectors of sample covariance (joint with A. Naumov and V. Ulyanov), Séminaire de Statistique, Université de Toulouse, Institut de Mathématiques, France, November 7, 2017.

V. Spokoiny, Gaussian approximation for a probability of a ball, Structural Learning Seminar, Russian Academy of Sciences, Kharkevich Institute for Information Transmission Problems, PreMoLab, Moscow, June 5, 2017.

V. Spokoiny, Gaussian approximation of the squared norm of a high dimensional vector, Structural Learning Seminar, Russian Academy of Sciences, Kharkevich Institute for Information Transmission Problems, PreMoLab, Moscow, May 18, 2017.

V. Spokoiny, Nonparametric estimation: Parametric view, Advanced Statistical Methods, February 7  22, 2017, Independent University of Moscow, Russian Federation.

V. Spokoiny, Quantification of uncertainty in estimation of spectral projectors, CIMWIAS Workshop ``Topics in Applied Analysis and Optimisation'', December 6  8, 2017, International Center for Mathematics, University of Lisbon, Portugal, December 6, 2017.

V. Spokoiny, Structural learning, Structural Learning Seminar, Russian Academy of Sciences, Kharkevich Institute for Information Transmission Problems, PreMoLab, Moscow, October 25, 2017.

V. Spokoiny, Subset selection using the smallest accepted rule, Structure Learning Seminar, Russian Academy of Sciences, Kharkevich Institute for Information Transmission Problems, PreMoLab, Moscow, April 6, 2017.

K. Tabelow, Ch. D'alonzo, L. Ruthotto, M.F. Callaghan, N. Weiskopf, J. Polzehl, S. Mohammadi, Removing the estimation bias due to the noise floor in multiparameter maps, The International Society for Magnetic Resonance in Medicine (ISMRM) 25th Annual Meeting & Exhibition, Honolulu, USA, April 22  27, 2017.

K. Tabelow, Ch. D'alonzo, J. Polzehl, Toward invivo histology of the brain, 2nd Leibniz MMs Days 2017, Technische Informationsbibliothek, Hannover, February 22  24, 2017.

K. Tabelow, Adaptive smoothing of multiparameter maps, Berlin Symposium 2017: Modern Statistical Methods From Data to Knowledge, December 14  15, 2017, organized by Indiana Laboratory of Biostatistical Analysis of Large Data with Structure (ILBALDS), Berlin, December 14, 2017.

K. Tabelow, High resolution MRI by variance and bias reduction, Channel Network Conference 2017 of the International Biometric Society (IBS), April 24  26, 2017, Hasselt University, Diepenbeek, Belgium, April 25, 2017.

K. Tabelow, MRI data models at low SNR, 2nd Leibniz MMs Days2017, February 22  24, 2017, Leibniz Informationszentrum Technik und Naturwissenschaften Technische Informationsbibliothek, Hannover, February 24, 2017, DOI 10.5446/21910 .

K. Tabelow, To smooth or not to smooth in fMRI, Cognitive Neuroscience Seminar, Universitätsklinikum HamburgEppendorf, Institut für Computational Neuroscience, April 4, 2017.
External Preprints

A. Ivanova, P. Dvurechensky, A. Gasnikov, Composite optimization for the resource allocation problem, Preprint no. arXiv:1810.00595, Cornell University Library, arXiv.org, 2018.
Abstract
In this paper we consider resource allocation problem stated as a convex minimization problem with linear constraints. To solve this problem, we use gradient and accelerated gradient descent applied to the dual problem and prove the convergence rate both for the primal iterates and the dual iterates. We obtain faster convergence rates than the ones known in the literature. We also provide economic interpretation for these two methods. This means that iterations of the algorithms naturally correspond to the process of price and production adjustment in order to obtain the desired production volume in the economy. Overall, we show how these actions of the economic agents lead the whole system to the equilibrium. 
C. A. Uribe, D. Dvinskikh, P. Dvurechensky, A. Gasnikov, A. Nedić, Distributed computation of Wasserstein barycenters over networks, Preprint no. arXiv:1803.02933, Cornell University Library, arXiv.org, 2018.

B. Hofmann, S. Kindermann, P. Mathé, Penaltybased smoothness conditions in convex variational regularization, Preprint no. arXiv:1805.01320, Cornell University Library, arXiv.org, 2018.
Abstract
The authors study Tikhonov regularization of linear illposed problems with a general convex penalty defined on a Banach space. It is well known that the error analysis requires smoothness assumptions. Here such assumptions are given in form of inequalities involving only the family of noisefree minimizers along the regularization parameter and the (unknown) penaltyminimizing solution. These inequalities control, respectively, the defect of the penalty, or likewise, the defect of the whole Tikhonov functional. The main results provide error bounds for a Bregman distance, which split into two summands: the first smoothnessdependent term does not depend on the noise level, whereas the second term includes the noise level. This resembles the situation of standard quadratic Tikhonov regularization Hilbert spaces. It is shown that variational inequalities, as these were studied recently, imply the validity of the assumptions made here. Several examples highlight the results in specific applications. 
Y. Nesterov, A. Gasnikov, S. Guminov, P. Dvurechensky, Primaldual accelerated gradient descent with line search for convex and nonconvex optimization problems, Preprint no. arXiv:1809.05895, Cornell University Library, arXiv.org, 2018.
Abstract
In this paper a new variant of accelerated gradient descent is proposed. The proposed method does not require any information about the objective function, uses exact line search for the practical accelerations of convergence, converges according to the wellknown lower bounds for both convex and nonconvex objective functions, possesses primaldual properties and can be applied in the noneuclidian setup. As far as we know this is the first such method possessing all of the above properties at the same time. We demonstrate how in practice one can efficiently use the combination of linesearch and primalduality by considering a convex optimization problem with a simple structure (for example, affinely constrained). 
CH. Bayer, R. Tempone, S. Wolfers, Pricing american options by exercise rate optimization, Preprint no. arXiv:1809.07300, Cornell University Library, arXiv.org, 2018.
Abstract
We present a novel method for the numerical pricing of American options based on Monte Carlo simulation and optimization of exercise strategies. Previous solutions to this problem either explicitly or implicitly determine socalled optimal emphexercise regions, which consist of points in time and space at which the option is exercised. In contrast, our method determines emphexercise rates of randomized exercise strategies. We show that the supremum of the corresponding stochastic optimization problem provides the correct option price. By integrating analytically over the random exercise decision, we obtain an objective function that is differentiable with respect to perturbations of the exercise rate even for finitely many sample paths. Starting in a neutral strategy with constant exercise rate then allows us to globally optimize this function in a gradual manner. Numerical experiments on vanilla put options in the multivariate BlackScholes model and preliminary theoretical analysis underline the efficiency of our method both with respect to the number of timediscretization steps and the required number of degrees of freedom in the parametrization of exercise rates. Finally, the flexibility of our method is demonstrated by numerical experiments on max call options in the BlackScholes model and vanilla put options in Heston model and the nonMarkovian rough Bergomi model. 
P. Dvurechensky, A. Gasnikov, E. Gorbunov, An accelerated directional derivative method for smooth stochastic convex optimization, Preprint no. arXiv: 1804.02394, Cornell University Library, arXiv.org, 2018.
Abstract
We consider smooth stochastic convex optimization problems in the context of algorithms which are based on directional derivatives of the objective function. This context can be considered as an intermediate one between derivativefree optimization and gradientbased optimization. We assume that at any given point and for any given direction, a stochastic approximation for the directional derivative of the objective function at this point and in this direction is available with some additive noise. The noise is assumed to be of an unknown nature, but bounded in the absolute value. We underline that we consider directional derivatives in any direction, as opposed to coordinate descent methods which use only derivatives in coordinate directions. For this setting, we propose a nonaccelerated and an accelerated directional derivative method and provide their complexity bounds. Despite that our algorithms do not use gradient information, our nonaccelerated algorithm has a complexity bound which is, up to a factor logarithmic in problem dimension, similar to the complexity bound of gradientbased algorithms. Our accelerated algorithm has a complexity bound which coincides with the complexity bound of the accelerated gradientbased algorithm up to a factor of square root of the problem dimension, whereas for existing directional derivative methods this factor is of the order of problem dimension. We also extend these results to strongly convex problems. Finally, we consider derivativefree optimization as a particular case of directional derivative optimization with noise in the directional derivative and obtain complexity bounds for nonaccelerated and accelerated derivativefree methods. Complexity bounds for these algorithms inherit the gain in the dimension dependent factors from our directional derivative methods. 
P. Dvurechensky, A. Gasnikov, E. Gorbunov, An accelerated method for derivativefree smooth stochastic convex optimization, Preprint no. arXiv: 1802.09022, Cornell University Library, arXiv.org, 2018.
Abstract
We consider an unconstrained problem of minimization of a smooth convex function which is only available through noisy observations of its values, the noise consisting of two parts. Similar to stochastic optimization problems, the first part is of a stochastic nature. On the opposite, the second part is an additive noise of an unknown nature, but bounded in the absolute value. In the twopoint feedback setting, i.e. when pairs of function values are available, we propose an accelerated derivativefree algorithm together with its complexity analysis. The complexity bound of our derivativefree algorithm is only by a factor of n??? larger than the bound for accelerated gradientbased algorithms, where n is the dimension of the decision variable. We also propose a nonaccelerated derivativefree algorithm with a complexity bound similar to the stochasticgradientbased algorithm, that is, our bound does not have any dimensiondependent factor. Interestingly, if the solution of the problem is sparse, for both our algorithms, we obtain better complexity bound if the algorithm uses a 1norm proximal setup, rather than the Euclidean proximal setup, which is a standard choice for unconstrained problems. 
P. Dvurechensky, A. Gasnikov, A. Kroshnin, Computational optimal transport: Complexity by accelerated gradient descent is better than by Sinkhorn's algorithm, Preprint no. arXiv: 1802.04367, Cornell University Library, arXiv.org, 2018.

P. Dvurechensky, A. Gasnikov, F. Stonyakin, A. Titov, Generalized mirror prox: Solving variational inequalities with monotone operator, inexact Oracle, and unknown Hölder parameters, Preprint no. arXiv:1806.05140, Cornell University Library, arXiv.org, 2018.

P. Dvurechensky, D. Dvinskikh, A. Gasnikov, C.A. Uribe, A. Nedić, Decentralize and randomize: Faster algorithm for Wasserstein barycenters, Preprint no. arXiv:1806.03915, Cornell University Library, arXiv.org, 2018.
Abstract
We study the problem of decentralized distributed computation of a discrete approximation for regularized Wasserstein barycenter of a finite set of continuous probability measures distributedly stored over a network. Particularly, we assume that there is a network of agents/machines/computers where each agent holds a private continuous probability measure, and seeks to compute the barycenter of all the measures in the network by getting samples from its local measure and exchanging information with its neighbors. Motivated by this problem, we develop and theoretically analyze a novel accelerated primaldual stochastic gradient method for general stochastic convex optimization problems with linear equality constraints. Then, we apply this method to the decentralized distributed optimization setting to propose a new algorithm for the distributed semidiscrete regularized Wasserstein barycenter problem. The proposed algorithm can be executed over arbitrary networks that are undirected, connected and static, using the local information only. Moreover, we show explicit nonasymptotic complexity in terms of the problem parameters. Finally, we show the effectiveness of our method on the distributed computation of the regularized Wasserstein barycenter of univariate Gaussian and von Mises distributions, as well as on some applications to image aggregation. 
P. Mathé, Bayesian inverse problems with noncommuting operators, Preprint no. arXiv:1801.09540, Cornell University Library, arXiv.org, 2018.
Abstract
The Bayesian approach to illposed operator equations in Hilbert space recently gained attraction. In this context, and when the prior distribution is Gaussian, then two operators play a significant role, the one which governs the operator equation, and the one which describes the prior covariance. Typically it is assumed that these operators commute. Here we extend this analysis to noncommuting operators, replacing the commutativity assumption by a link condition. We discuss its relation to the commuting case, and we indicate that this allows to use interpolation type results to obtain tight bounds for the contraction of the posterior Gaussian distribution towards the data generating element. 
N. Buzun, V. Avanesov, Bootstrap for change point detection, Preprint no. arXiv:1710.07285, Cornell University Library, arXiv.org, 2017.

A. Bayandina, P. Dvurechensky, A. Gasnikov, F. Stonyakin, A. Titov, Mirror descent and convex optimization problems with nonsmooth inequality constraints, Preprint no. arXiv:1710.06612, Cornell University Library, arXiv.org, 2017.
Abstract
We consider the problem of minimization of a convex function on a simple set with convex nonsmooth inequality constraint and describe firstorder methods to solve such problems in different situations: smooth or nonsmooth objective function; convex or strongly convex objective and constraint; deterministic or randomized information about the objective and constraint. We hope that it is convenient for a reader to have all the methods for different settings in one place. Described methods are based on Mirror Descent algorithm and switching subgradient scheme. One of our focus is to propose, for the listed different settings, a Mirror Descent with adaptive stepsizes and adaptive stopping rule. This means that neither stepsize nor stopping rule require to know the Lipschitz constant of the objective or constraint. We also construct Mirror Descent for problems with objective function, which is not Lipschitz continuous, e.g. is a quadratic function. Besides that, we address the problem of recovering the solution of the dual problem. 
L.T. Ding, P. Mathé, Minimax rates for statistical inverse problems under general source conditions, Preprint no. arXiv:1707.01706, Cornell University Library, arXiv.org, 2017.
Abstract
We describe the minimax reconstruction rates in linear illposed equations in Hilbert space when smoothness is given in terms of general source sets. The underlying fundamental result, the minimax rate on ellipsoids, is proved similarly to the seminal study by D. L. Donoho, R. C. Liu, and B. MacGibbon, it Minimax risk over hyperrectangles, and implications, Ann. Statist. 18, 1990. These authors highlighted the special role of the truncated series estimator, and for such estimators the risk can explicitly be given. We provide several examples, indicating results for statistical estimation in illposed problems in Hilbert space. 
J. Ebert, V. Spokoiny, A. Suvorikova , Construction of nonasymptotic confidence sets in 2Wasserstein space, Preprint no. arXiv:1703.03658, Cornell University Library, arXiv.org, 2017.
Abstract
In this paper, we consider a probabilistic setting where the probability measures are considered to be random objects. We propose a procedure of construction nonasymptotic confidence sets for empirical barycenters in 2Wasserstein space and develop the idea further to construction of a nonparametric twosample test that is then applied to the detection of structural breaks in data with complex geometry. Both procedures mainly rely on the idea of multiplier bootstrap (Spokoiny and Zhilova (2015), Chernozhukov et al. (2014)). The main focus lies on probability measures that have commuting covariance matrices and belong to the same scatterlocation family: we proof the validity of a bootstrap procedure that allows to compute confidence sets and critical values for a Wassersteinbased twosample test. 
B. Gess, M. Maurelli, Wellposedness by noise for scalar conservation laws, Preprint no. arXiv:1701.05393, Cornell University Library, arXiv.org, 2017.
Abstract
We consider stochastic scalar conservation laws with spatially inhomogeneous flux. The regularity of the flux function with respect to its spatial variable is assumed to be low, so that entropy solutions are not necessarily unique in the corresponding deterministic scalar conservation law. We prove that perturbing the system by noise leads to wellposedness. 
F. Götze, A. Naumov, V. Spokoiny, V. Ulyanov, Gaussian comparison and anticoncentration inequalities for norms of Gaussian random elements, Preprint no. arXiv:1708.08663, Cornell University Library, arXiv.org, 2017.
Abstract
We derive the bounds on the Kolmogorov distance between probabilities of two Gaussian elements to hit a ball in a Hilbert space. The key property of these bounds is that they are dimensionalfree and depend on the nuclear (Schattenone) norm of the difference between the covariance operators of the elements. We are also interested in the anticoncentration bound for a squared norm of a noncentered Gaussian element in a Hilbert space. All bounds are sharp and cannot be improved in general. We provide a list of motivation examples and applications for the derived results as well. 
A. Naumov, V. Spokoiny, V. Ulyanov, Bootstrap confidence sets for spectral projectors of sample covariance, Preprint no. arXiv:1703.00871, Cornell University Library, arXiv.org, 2017.
Abstract
Let X1,?,Xn be i.i.d. sample in ?p with zero mean and the covariance matrix ?. The problem of recovering the projector onto an eigenspace of ? from these observations naturally arises in many applications. Recent technique from [Koltchinskii, Lounici, 2015] helps to study the asymptotic distribution of the distance in the Frobenius norm ?Pr?P?r?2 between the true projector Pr on the subspace of the rth eigenvalue and its empirical counterpart P?r in terms of the effective rank of ?. This paper offers a bootstrap procedure for building sharp confidence sets for the true projector Pr from the given data. This procedure does not rely on the asymptotic distribution of ?Pr?P?r?2 and its moments. It could be applied for small or moderate sample size n and large dimension p. The main result states the validity of the proposed procedure for finite samples with an explicit error bound for the error of bootstrap approximation. This bound involves some new sharp results on Gaussian comparison and Gaussian anticoncentration in highdimensional spaces. Numeric results confirm a good performance of the method in realistic examples. 
I. Silin, V. Spokoiny, Bayesian inference for spectral projectors of covariance matrix, Preprint no. arXiv:1711.11532, Cornell University Library, arXiv.org, 2017.

CH. Bayer, P. Friz, P. Gassiat, J. Martin, B. Stemper , A regularity structure for rough volatility, Preprint no. arXiv:1710.07481, Cornell University Library, arXiv.org, 2017.
Abstract
A new paradigm recently emerged in financial modelling: rough (stochastic) volatility, first observed by Gatheral et al. in highfrequency data, subsequently derived within market microstructure models, also turned out to capture parsimoniously key stylized facts of the entire implied volatility surface, including extreme skews that were thought to be outside the scope of stochastic volatility. On the mathematical side, Markovianity and, partially, semimartingality are lost. In this paper we show that Hairer's regularity structures, a major extension of rough path theory, which caused a revolution in the field of stochastic partial differential equations, also provides a new and powerful tool to analyze rough volatility models. 
P. Dvurechensky, A. Gasnikov, A. Lagunovskaya, Parallel algorithms and probability of large deviation for stochastic optimization problems, Preprint no. arXiv:1701.01830, Cornell University Library, arXiv.org, 2017.
Abstract
We consider convex stochastic optimization problems under different assumptions on the properties of available stochastic subgradient. It is known that, if the value of the objective function is available, one can obtain, in parallel, several independent approximate solutions in terms of the objective residual expectation. Then, choosing the solution with the minimum function value, one can control the probability of large deviation of the objective residual. On the contrary, in this short paper, we address the situation, when the value of the objective function is unavailable or is too expensive to calculate. Under "`lighttail"' assumption for stochastic subgradient and in general case with moderate large deviation probability, we show that parallelization combined with averaging gives bounds for probability of large deviation similar to a serial method. Thus, in these cases, one can benefit from parallel computations and reduce the computational time without loss in the solution quality. 
P. Dvurechensky, A. Gasnikov, A. Tiurin, Randomized similar triangles method: A unifying framework for accelerated randomized optimization methods (coordinate descent, directional search, derivativefree method), Preprint no. arXiv:1707.08486, Cornell University Library, arXiv.org, 2017.
Abstract
In this paper, we consider smooth convex optimization problems with simple constraints and inexactness in the oracle information such as value, partial or directional derivatives of the objective function. We introduce a unifying framework, which allows to construct different types of accelerated randomized methods for such problems and to prove convergence rate theorems for them. We focus on accelerated random blockcoordinate descent, accelerated random directional search, accelerated random derivativefree method and, using our framework, provide their versions for problems with inexact oracle information. Our contribution also includes accelerated random blockcoordinate descent with inexact oracle and entropy proximal setup as well as derivativefree version of this method. 
P. Dvurechensky, S. Omelchenko, A. Tiurin, Adaptive similar triangles method: A stable alternative to Sinkhorn's algorithm for regularized optimal transport, Preprint no. arXiv:1706.07622, Cornell University Library, arXiv.org, 2017.
Abstract
In this paper, we are motivated by two important applications: entropyregularized optimal transport problem and road or IP traffic demand matrix estimation by entropy model. Both of them include solving a special type of optimization problem with linear equality constraints and objective given as a sum of an entropy regularizer and a linear function. It is known that the stateoftheart solvers for this problem, which are based on Sinkhorn's method (also known as RSA or balancing method), can fail to work, when the entropyregularization parameter is small. We consider the above optimization problem as a particular instance of a general strongly convex optimization problem with linear constraints. We propose a new algorithm to solve this general class of problems. Our approach is based on the transition to the dual problem. First, we introduce a new accelerated gradient method with adaptive choice of gradient's Lipschitz constant. Then, we apply this method to the dual problem and show, how to reconstruct an approximate solution to the primal problem with provable convergence rate. We prove the rate O(1/k2), k being the iteration counter, both for the absolute value of the primal objective residual and constraints infeasibility. Our method has similar to Sinkhorn's method complexity of each iteration, but is faster and more stable numerically, when the regularization parameter is small. We illustrate the advantage of our method by numerical experiments for the two mentioned applications. We show that there exists a threshold, such that, when the regularization parameter is smaller than this threshold, our method outperforms the Sinkhorn's method in terms of computation time. 
P. Dvurechensky, Gradient method with inexact oracle for composite nonconvex optimization, Preprint no. arXiv:1703.09180, Cornell University Library, arXiv.org, 2017.
Abstract
In this paper, we develop new firstorder method for composite nonconvex minimization problems with simple constraints and inexact oracle. The objective function is given as a sum of "`hard"', possibly nonconvex part, and "`simple"' convex part. Informally speaking, oracle inexactness means that, for the "`hard"' part, at any point we can approximately calculate the value of the function and construct a quadratic function, which approximately bounds this function from above. We give several examples of such inexactness: smooth nonconvex functions with inexact Höldercontinuous gradient, functions given by auxiliary uniformly concave maximization problem, which can be solved only approximately. For the introduced class of problems, we propose a gradienttype method, which allows to use different proximal setup to adapt to geometry of the feasible set, adaptively chooses controlled oracle error, allows for inexact proximal mapping. We provide convergence rate for our method in terms of the norm of generalized gradient mapping and show that, in the case of inexact Höldercontinuous gradient, our method is universal with respect to Hölder parameters of the problem. Finally, in a particular case, we show that small value of the norm of generalized gradient mapping at a point means that a necessary condition of local minimum approximately holds at that point. 
P. Mathé, B. Hofmann, Tikhonov regularization with oversmoothing penalty for nonlinear illposed problems in Hilbert scales, Preprint no. arXiv:1705.03289, Cornell University Library, arXiv.org, 2017.
Research Groups
 Partial Differential Equations
 Laser Dynamics
 Numerical Mathematics and Scientific Computing
 Nonlinear Optimization and Inverse Problems
 Interacting Random Systems
 Stochastic Algorithms and Nonparametric Statistics
 Thermodynamic Modeling and Analysis of Phase Transitions
 Nonsmooth Variational Problems and Operator Equations