MinMax Methods for Optimal Transport and Beyond: Regularization, Approximation and Numerics. - Luca De Gennaro Aquino; Stephan Eckstein (NeurIPS, 2020)

Lipschitz neural networks are dense in the set of all Lipschitz functions. - Stephan Eckstein (CoRR, 2020)

Robust Pricing and Hedging of Options on Multiple Assets and Its Numerics. - Stephan Eckstein; Gaoyue Guo; Tongseok Lim; Jan Oblój (SIAM J. Financial Math., 2021)

Marginal and Dependence Uncertainty: Bounds, Optimal Transport, and Sharpness. - Daniel Bartl; Michael Kupper; Thibaut Lux; Antonis Papapantoleon; Stephan Eckstein (SIAM J. Control. Optim., 2022)

Quantitative Stability of Regularized Optimal Transport and Convergence of Sinkhorn's Algorithm. - Stephan Eckstein; Marcel Nutz (SIAM J. Math. Anal., 2022)

Estimating the Rate-Distortion Function by Wasserstein Gradient Descent. - Yibo Yang; Stephan Eckstein; Marcel Nutz; Stephan Mandt (NeurIPS, 2023)

Dimensionality Reduction and Wasserstein Stability for Kernel Regression. - Stephan Eckstein; Armin Iske; Mathias Trabs (J. Mach. Learn. Res., 2023)

Time-Causal VAE: Robust Financial Time Series Generator. - Beatrice Acciaio; Stephan Eckstein; Songyan Hou (CoRR, 2024)

Convergence Rates for Regularized Optimal Transport via Quantization. - Stephan Eckstein; Marcel Nutz (Math. Oper. Res., 2024)