Preprints
R. Salac, M.Kupper and T. Sutter Asymptotic Optimality in Data-driven Decision Making [arXiv]
A. Wolter, T. Sutter, A Two-Timescale Primal-Dual Framework for Reinforcement Learning via Online Dual Variable Guidance [arXiv]
M. Li, D. Kuhn and T. Sutter, Towards Optimal Offline Reinforcement Learning [arXiv]
L. Pleiss, T. Sutter and M. Schiffer, Reliability-Adjusted Prioritized Experience Replay [arXiv]
M. Li, D. Kuhn and T. Sutter, Policy Gradient Algorithms for Robust MDPs with Non-Rectangular Uncertainty Sets [arXiv]
A. Ganguly and T. Sutter, Optimal learning via Moderate Deviations Theory [arXiv]
Journals
N. Schmid, M. Fochesato, S. Li, T. Sutter and J. Lygeros, Computing Optimal Joint Chance Constrained Control Policies, accepted at IEEE Transactions on Automatic Control, 2025 [DOI, arXiv]
T. Sutter, B. van Parys and D. Kuhn, A Pareto Dominance Principle for Data-Driven Optimization, Operations Research, 2024, vol. 72, no. 5, pp. 1976-1999 [DOI, arXiv]
N. Schmid, M. Fochesato, T. Sutter and J. Lygeros, Joint Chance Constrained Optimal Control via Linear Programming, IEEE Control Systems Letters, vol. 8, pp. 736-74, 2024 [DOI, arXiv]
W. Jongeneel, T. Sutter, and D. Kuhn, Efficient Learning of a Linear Dynamical System with Stability Guarantees, IEEE Transactions on Automatic Control, 2023, vol.68, pp.2790-2804 [DOI, arXiv]
W. Jongeneel, T. Sutter, and D. Kuhn, Topological Linear System Identification via Moderate Deviations Theory, IEEE Control Systems Letters, 2021, vol. 6, pp. 307-312 [DOI, arXiv]
T. Sutter, D. Sutter, P. Mohajerin Esfahani and J. Lygeros, Generalized maximum entropy estimation, Journal on Machine Learning Research, vol. 20, 2019, [DOI, arXiv]
P. Mohajerin Esfahani, T. Sutter, D. Kuhn and J. Lygeros, From Infinite to Finite Programs: Explicit Error Bounds with an Application to Approximate Dynamic Programming, SIAM Journal on Optimization, 2018, vol. 28, no. 3, pp. 1968-1998, [DOI, arXiv]
T. Sutter, D. Sutter and J. Lygeros, Capacity of Random Channels with Large Alphabets, Advances in Mathematics of Communications, 2017, vol. 11, no. 4, pp. 813 - 835, [DOI, arXiv]
A. Kamoutsi, T. Sutter, P. Mohajerin Esfahani and J. Lygeros, On Infinite Linear Programming and the Moment Approach to Deterministic Infinite Horizon Discounted Optimal Control Problems, IEEE Control Systems Letters, vol. 1, no. 1, 2017, [DOI, arXiv]
T. Sutter, A. Ganguly, and H. Koeppl, A Variational Approach to Path Estimation and Parameter Inference of Hidden Diffusion Processes, Journal on Machine Learning Research, vol. 17, 2016, [DOI, arXiv]
D. Sutter, T. Sutter, P. Mohajerin Esfahani and R. Renner, Efficient Approximation of Quantum Channel Capacities, IEEE Transactions on Information Theory, vol. 62, no. 1, 2016, [DOI, arXiv]
T. Sutter and J. Lygeros, Signals and Systems II: A Flipped Classroom Experiment for Undergraduate Control Education, ASME Control and Dynamics Magazine, 2016, [DOI, Paper]
T. Sutter, D. Sutter, P. Mohajerin Esfahani and J. Lygeros, Efficient Approximation of Channel Capacities, IEEE Transactions on Information Theory, vol. 61, no. 4, 2015, [DOI, arXiv]
P. Mohajerin Esfahani, T. Sutter, and J. Lygeros, Performance Bounds for the Scenario Approach and an Extension to a Class of Non-convex Programs, IEEE Transactions on Automatic Control, vol. 60, no. 1, 2015, [DOI, arXiv] ==> George S. Axelby Outstanding Paper Award 2016(picture)
T. Sutter, D. Chatterjee, F. Ramponi, and J. Lygeros, Isospectral flows on a class of finite-dimensional Jacobi matrices, Systems and Control Letters, vol. 62, no. 5, 2013, [DOI, arXiv]
Peer Reviewed Conferences
D. Boetius, S. Leue, T. Sutter, Probabilistic Verification of Neural Networks using Branch and Bound, International Conference on Machine Learning (ICML), 2025 (to appear) [arXiv]
A. Kamoutsi, P. Schmitt-Förster, T. Sutter, V. Cevher, J. Lygeros, Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces, Conference on Neural Information Processing Systems (NeurIPS), 2024 [DOI, arXiv]
F. Petersen, C. Borgelt, T. Sutter, H. Kuehne, O. Deussen, S. Ermon, Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms, Conference on Neural Information Processing Systems (NeurIPS), 2024 [DOI, arXiv]
P. Schmitt-Förster and T. Sutter, Regularized Q-learning through Robust Averaging, International Conference on Machine Learning (ICML), 2024, [DOI, arXiv]
U. Schlegel, D. Keim and T. Sutter, Finding the DeepDream for Time Series: Activation Maximization for Univariate Time Series, Workshop on Explainable AI for Time Series and Data Streams (TempXAI 2024), 2024, [Paper]
Y. Rychener, D. Kuhn and T. Sutter, End-to-End Learning for Stochastic Optimization: A Bayesian Perspective, International Conference on Machine Learning (ICML), 2023 [DOI], [arXiv]
D. Boetius, S. Leue and T. Sutter, A Robust Optimisation Perspective on Counterexample-Guided Repair of Neural Networks, International Conference on Machine Learning (ICML), 2023 [DOI], [arXiv]
F. Petersen, T. Sutter, C.Borgelt, D.Huh, H.Kuehne, Y.Sun, O.Deussen, ISAAC Newton: Input-based Approximate Curvature for Newton's Method, International Conference on Learning Representations (ICLR), 2023 [DOI], [video]
T. Sutter, A. Krause and D. Kuhn, Robust Generalization despite Distribution Shift via Minimum Discriminating Information, Conference on Neural Information Processing Systems (NeurIPS), 2021 [DOI, arXiv]
M. Li, T. Sutter and D. Kuhn, Distributionally Robust Optimization with Markovian Data, International Conference on Machine Learning (ICML), 2021, [DOI, arXiv]
T. Sutter, A. Kamoutsi, P. Mohajerin Esfahani and J. Lygeros, Data-driven approximate dynamic programming: A linear programming approach, IEEE Conference on Decision and Control (CDC), 2017, [DOI]
M. Thely, T. Sutter, P. Mohajerin Esfahani and J. Lygeros, Maximum Entropy Estimation via Gauss-LP Quadratures, IFAC World Congress, 2017, [DOI]
T. Sutter, P. Mohajerin Esfahani and J. Lygeros, Approximation of constrained average cost Markov control processes, IEEE Conference on Decision and Control (CDC), 2014, [DOI]
T. Sutter, D. Sutter, J. Lygeros, Asymptotic capacity of a random channel, 52nd Annual Allerton Conference on Communications, Control, and Computing, 2014, [DOI]
D. Sutter, T. Sutter, P. Esfahani, J. Lygeros, Efficient approximation of discrete memoryless channel capacities, IEEE International Symposium on Information Theory (ISIT), 2014, [DOI]
T. Sutter, D. Sutter, P. Esfahani, J. Lygeros, Capacity approximation of memoryless channels with countable output alphabets, IEEE International Symposium on Information Theory (ISIT), 2014, [DOI]
Doctoral / Master / Bachelor Theses
PhD thesis: Convex programming in optimal control and information theory, advisor John Lygeros, committee: Daniel Kuhn & Sean Meyn
Master thesis: Variational Inference for State Dependent Diffusion Processes, advisor Arnab Ganguly
Semester project: Isospectral Flows on a Class of Finite-Dimensional Jacobi Matrices, advisor John Lygeros & Debasish Chatterjee & Federico Ramponi
Bachelor thesis: A Dynamical Approach to Create Different Juggling Patterns Using Chaos, advisor Raffaello D'Andrea