J.J. Zhu (WIAS Berlin)
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In this talk, I will first introduce our works in establishing computational
algorithms for robust learning and optimization algorithms, highlighting the use
of kernel methods and optimal transport in treating the ubiquitous data
distribution shift that plagues modern-day machine learning. I will do so by
presenting perspectives from numerical optimization and optimal control,
statistical learning theory, function spaces and approximations. In the latter
half, the talk will focus on the outlook on multi-stage problems and topics
beyond the classical notion of robustness, highlighting technical limitations,
what we already know, and what we do not yet know.