| Computation of optimal transport and related hedging problems via penalization and neural networks S Eckstein, M Kupper Applied Mathematics & Optimization 83 (2), 639-667, 2021 | 51 | 2021 |
| Quantitative Stability of Regularized Optimal Transport and Convergence of Sinkhorn's Algorithm S Eckstein, M Nutz SIAM Journal on Mathematical Analysis 54 (6), 5922-5948, 2022 | 35 | 2022 |
| Robust risk aggregation with neural networks S Eckstein, M Kupper, M Pohl Mathematical finance 30 (4), 1229-1272, 2020 | 35 | 2020 |
| Robust pricing and hedging of options on multiple assets and its numerics S Eckstein, G Guo, T Lim, J Obłój SIAM Journal on Financial Mathematics 12 (1), 158-188, 2021 | 34 | 2021 |
| Extended Laplace principle for empirical measures of a Markov chain S Eckstein Advances in Applied Probability 51 (1), 136-167, 2019 | 16 | 2019 |
| Computational methods for adapted optimal transport S Eckstein, G Pammer The Annals of Applied Probability 34 (1A), 675-713, 2024 | 14 | 2024 |
| Convergence rates for regularized optimal transport via quantization S Eckstein, M Nutz Mathematics of Operations Research, 2023 | 12 | 2023 |
| Marginal and dependence uncertainty: bounds, optimal transport, and sharpness D Bartl, M Kupper, T Lux, A Papapantoleon, S Eckstein SIAM Journal on Control and Optimization 60 (1), 410-434, 2022 | 11 | 2022 |
| Limits of random walks with distributionally robust transition probabilities D Bartl, S Eckstein, M Kupper | 11 | 2021 |
| Minmax methods for optimal transport and beyond: Regularization, approximation and numerics L De Gennaro Aquino, S Eckstein Advances in Neural Information Processing Systems 33, 13818-13830, 2020 | 10 | 2020 |
| Marginal and dependence uncertainty: bounds, optimal transport, and sharpness D Bartl, M Kupper, T Lux, A Papapantoleon, S Eckstein arXiv preprint arXiv:1709.00641, 2017 | 8 | 2017 |
| Martingale transport with homogeneous stock movements S Eckstein, M Kupper Quantitative Finance 21 (2), 271-280, 2021 | 7 | 2021 |
| Stability and sample complexity of divergence regularized optimal transport E Bayraktar, S Eckstein, X Zhang arXiv preprint arXiv:2212.00367, 2022 | 6 | 2022 |
| Quantitative stability of regularized optimal transport S Eckstein, M Nutz arXiv preprint arXiv:2110.06798, 2021 | 5 | 2021 |
| Lipschitz neural networks are dense in the set of all Lipschitz functions S Eckstein arXiv preprint arXiv:2009.13881, 2020 | 4 | 2020 |
| Dimensionality reduction and wasserstein stability for kernel regression S Eckstein, A Iske, M Trabs Journal of Machine Learning Research 24 (334), 1-35, 2023 | 2 | 2023 |
| Estimating the rate-distortion function by Wasserstein gradient descent Y Yang, S Eckstein, M Nutz, S Mandt Advances in Neural Information Processing Systems 36, 2024 | 1 | 2024 |
| Optimal transport and Wasserstein distances for causal models P Cheridito, S Eckstein arXiv preprint arXiv:2303.14085, 2023 | 1 | 2023 |
| THE ANNALS N DEB, R MUKHERJEE, S MUKHERJEE, M YUAN, Y KIFER, X ZHANG, ... The Annals of Applied Probability 34 (1A), 2024 | | 2024 |
| Hilbert's projective metric for functions of bounded growth and exponential convergence of Sinkhorn's algorithm S Eckstein arXiv preprint arXiv:2311.04041, 2023 | | 2023 |
Michael KupperUniversity of KonstanzVerified email at uni-konstanz.de
