Publikationen

Vorträge, Poster

  • A. Pavlov, Bilevel Interior-point Differential Dynamic Programming, EUROPT2022 19th Workshop on Advances in Continuous Optimization, NOVA School of Science and Technology, Universidade Nova de Lisboa, Portugal, July 29, 2022.

  • H. Kremer, J.-J. Zhu, K. Muandet, B. Schölkopf, Functional generalized empirical likelihood estimation for conditional moment restrictions (spotlight, online talk), ICML 2022: 39th International Conference on Machine Learning (Online Event), July 18 - 23, 2022, Baltimore, USA, July 19, 2022.

  • J.-J. Zhu, Distributionally robust chance constrained programs using maximum mean discrepancy, SafeRL2021 in the framework of the Conference NeurIPS2021 (Online Event), University of California at Berkeley, USA, December 13, 2021.

  • J.-J. Zhu, Robust optimization and learning under distribution shift, Leibniz MMS Summer School ``Mathematical Methods for Machine Learning'', August 22 - 27, 2021, Schloss Dagstuhl, Leibniz-Zentrum für Informatik, Wadern.

Preprints im Fremdverlag

  • H. Abdulsamad, T. Dorau, B. Belousov, J.-J. Zhu, J. Peters, Distributionally robust trajectory optimization under uncertain dynamics via relative-entropy trust regions, Preprint no. arXiv:2103.15388, Cornell University Library, arXiv.org, 2021.

  • D. Agudelo-España, Y. Nemmour, B. Schölkopf, J.-J. Zhu, Shallow representation is deep: Learning uncertainty-aware and worst-case random feature dynamics, Preprint no. arXiv:2106.13066, Cornell University Library, arXiv.org, 2021.

  • Y. Nemmour, B. Schölkopf, J.-J. Zhu, Distributional robustness regularized scenario optimization with application to model predictive control, Preprint no. arXiv:2110.13588, Cornell University Library, arXiv.org, 2021.

  • J.-J. Zhu, Ch. Kouridi, Y. Nemmour, B. Schölkopf, Adversarially robust kernel smoothing, Preprint no. arXiv:2102.08474, Cornell University Library, arXiv.org, 2021.