| Performance bounds for the scenario approach and an extension to a class of non-convex programs PM Esfahani, T Sutter, J Lygeros IEEE Transactions on Automatic Control 60 (1), 46-58, 2014 | 155 | 2014 |
| From infinite to finite programs: Explicit error bounds with applications to approximate dynamic programming P Mohajerin Esfahani, T Sutter, D Kuhn, J Lygeros SIAM journal on optimization 28 (3), 1968-1998, 2018 | 47 | 2018 |
| Efficient approximation of quantum channel capacities D Sutter, T Sutter, PM Esfahani, R Renner IEEE Transactions on Information Theory 62 (1), 578-598, 2015 | 47 | 2015 |
| A Pareto Dominance Principle for Data-Driven Optimization T Sutter, BPG Van Parys, D Kuhn Operations Research, 2024 | 21* | 2024 |
| A variational approach to path estimation and parameter inference of hidden diffusion processes T Sutter, A Ganguly, H Koeppl Journal of Machine Learning Research 17 (190), 1-37, 2016 | 20 | 2016 |
| Efficient learning of a linear dynamical system with stability guarantees W Jongeneel, T Sutter, D Kuhn IEEE Transactions on Automatic Control 68 (5), 2790-2804, 2022 | 17 | 2022 |
| On infinite linear programming and the moment approach to deterministic infinite horizon discounted optimal control problems A Kamoutsi, T Sutter, PM Esfahani, J Lygeros IEEE control systems letters 1 (1), 134-139, 2017 | 16 | 2017 |
| Approximation of constrained average cost Markov control processes T Sutter, PM Esfahani, J Lygeros 53rd IEEE Conference on Decision and Control, 6597-6602, 2014 | 15 | 2014 |
| Data-driven approximate dynamic programming: A linear programming approach T Sutter, A Kamoutsi, PM Esfahani, J Lygeros 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 5174-5179, 2017 | 14 | 2017 |
| Generalized maximum entropy estimation T Sutter, D Sutter, PM Esfahani, J Lygeros Journal of Machine Learning Research 20 (138), 1-29, 2019 | 12 | 2019 |
| Robust generalization despite distribution shift via minimum discriminating information T Sutter, A Krause, D Kuhn Advances in Neural Information Processing Systems 34, 29754-29767, 2021 | 11 | 2021 |
| Distributionally robust optimization with markovian data M Li, T Sutter, D Kuhn International Conference on Machine Learning, 6493-6503, 2021 | 9 | 2021 |
| Efficient approximation of discrete memoryless channel capacities D Sutter, PM Esfahani, T Sutter, J Lygeros 2014 IEEE International Symposium on Information Theory, 2904-2908, 2014 | 8 | 2014 |
| Policy gradient algorithms for robust mdps with non-rectangular uncertainty sets M Li, D Kuhn, T Sutter arXiv preprint arXiv:2305.19004, 2023 | 7 | 2023 |
| End-to-end learning for stochastic optimization: A bayesian perspective Y Rychener, D Kuhn, T Sutter International Conference on Machine Learning, 29455-29472, 2023 | 6 | 2023 |
| Topological linear system identification via moderate deviations theory W Jongeneel, T Sutter, D Kuhn IEEE Control Systems Letters 6, 307-312, 2021 | 6 | 2021 |
| Quantum speedups for convex dynamic programming D Sutter, G Nannicini, T Sutter, S Woerner arXiv preprint arXiv:2011.11654, 2020 | 5 | 2020 |
| Capacity approximation of memoryless channels with countable output alphabets T Sutter, PM Esfahani, D Sutter, J Lygeros 2014 IEEE International Symposium on Information Theory, 2909-2913, 2014 | 5 | 2014 |
| A general framework for optimal Data-Driven optimization. October 2020 T Sutter, BPG Van Parys, D Kuhn URL http://arxiv. org/abs/2010 6606 (6.5), 6.6, 2010 | 5 | 2010 |
| Signals and systems II: A flipped classroom experiment in undergraduate control education T Sutter, J Lygeros Mechanical Engineering 138 (6), S17, 2016 | 4 | 2016 |
John LygerosProf. of Computation and Control, ETH Zurich在 ethz.ch 的电子邮件经过验证
