Isospectral flows on a class of finite-dimensional Jacobi matrices. - Tobias Sutter; Debasish Chatterjee; Federico Ramponi; John Lygeros (Syst. Control. Lett., 2013)

Asymptotic capacity of a random channel. - Tobias Sutter; David Sutter; John Lygeros (Allerton, 2014)

Approximation of constrained average cost Marks. - Tobias Sutter; Peyman Mohajerin Esfahani; John Lygeros (CDC, 2014)

Efficient approximation of discrete memoryless channel capacities. - David Sutter; Peyman Mohajerin Esfahani; Tobias Sutter; John Lygeros (ISIT, 2014)

Capacity approximation of memoryless channels with countable output alphabets. - Tobias Sutter; Peyman Mohajerin Esfahani; David Sutter; John Lygeros (ISIT, 2014)

Performance Bounds for the Scenario Approach and an Extension to a Class of Non-Convex Programs. - Peyman Mohajerin Esfahani; Tobias Sutter; John Lygeros (IEEE Trans. Autom. Control., 2015)

Efficient Approximation of Channel Capacities. - Tobias Sutter; David Sutter; Peyman Mohajerin Esfahani; John Lygeros (IEEE Trans. Inf. Theory, 2015)

A Variational Approach to Path Estimation and Parameter Inference of Hidden Diffusion Processes. - Tobias Sutter; Arnab Ganguly 001; Heinz Koeppl (J. Mach. Learn. Res., 2016)

Efficient Approximation of Quantum Channel Capacities. - David Sutter; Tobias Sutter; Peyman Mohajerin Esfahani; Renato Renner (IEEE Trans. Inf. Theory, 2016)

Data-driven approximate dynamic programming: A linear programming approach. - Tobias Sutter; Angeliki Kamoutsi; Peyman Mohajerin Esfahani; John Lygeros (CDC, 2017)

Capacity of random channels with large alphabets. - Tobias Sutter; David Sutter; John Lygeros (Adv. Math. Commun., 2017)

Convex programming in optimal control and information theory. - Tobias Sutter (CoRR, 2017)

On Infinite Linear Programming and the Moment Approach to Deterministic Infinite Horizon Discounted Optimal Control Problems. - Angeliki Kamoutsi; Tobias Sutter; Peyman Mohajerin Esfahani; John Lygeros (IEEE Control. Syst. Lett., 2017)

From Infinite to Finite Programs: Explicit Error Bounds with Applications to Approximate Dynamic Programming. - Peyman Mohajerin Esfahani; Tobias Sutter; Daniel Kuhn 001; John Lygeros (SIAM J. Optim., 2018)

Generalized Maximum Entropy Estimation. - Tobias Sutter; David Sutter; Peyman Mohajerin Esfahani; John Lygeros (J. Mach. Learn. Res., 2019)

Quantum Legendre-Fenchel Transform. - David Sutter; Giacomo Nannicini; Tobias Sutter; Stefan Woerner (CoRR, 2020)

Distributionally Robust Optimization with Markovian Data. - Mengmeng Li; Tobias Sutter; Daniel Kuhn 001 (ICML, 2021)

Robust Generalization despite Distribution Shift via Minimum Discriminating Information. - Tobias Sutter; Andreas Krause 001; Daniel Kuhn 001 (NeurIPS, 2021)

Topological Linear System Identification via Moderate Deviations Theory. - Wouter Jongeneel; Tobias Sutter; Daniel Kuhn 001 (IEEE Control. Syst. Lett., 2022)

ISAAC Newton: Input-based Approximate Curvature for Newton's Method. - Felix Petersen; Tobias Sutter; Christian Borgelt; Dongsung Huh; Hilde Kuehne; Yuekai Sun; Oliver Deussen (ICLR, 2023)

A Robust Optimisation Perspective on Counterexample-Guided Repair of Neural Networks. - David Boetius; Stefan Leue; Tobias Sutter (ICML, 2023)

End-to-End Learning for Stochastic Optimization: A Bayesian Perspective. - Yves Rychener; Daniel Kuhn 001; Tobias Sutter (ICML, 2023)

Policy Gradient Algorithms for Robust MDPs with Non-Rectangular Uncertainty Sets. - Mengmeng Li; Tobias Sutter; Daniel Kuhn 001 (CoRR, 2023)

Efficient Learning of a Linear Dynamical System With Stability Guarantees. - Wouter Jongeneel; Tobias Sutter; Daniel Kuhn 001 (IEEE Trans. Autom. Control., 2023)

Regularized Q-learning through Robust Averaging. - Peter Schmitt-Förster; Tobias Sutter (ICML, 2024)

Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces. - Angeliki Kamoutsi; Peter Schmitt-Förster; Tobias Sutter; Volkan Cevher; John Lygeros (NeurIPS, 2024)

Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms. - Felix Petersen; Christian Borgelt; Tobias Sutter; Hilde Kuehne; Oliver Deussen; Stefano Ermon (NeurIPS, 2024)

Finding the DeepDream for Time Series: Activation Maximization for Univariate Time Series. - Udo Schlegel; Daniel A. Keim; Tobias Sutter (TempXAI@PKDD/ECML, 2024)

Probabilistic Verification of Neural Networks using Branch and Bound. - David Boetius; Stefan Leue; Tobias Sutter (CoRR, 2024)

Joint Chance Constrained Optimal Control via Linear Programming. - Niklas Schmid; Marta Fochesato; Tobias Sutter; John Lygeros (IEEE Control. Syst. Lett., 2024)

A Pareto Dominance Principle for Data-Driven Optimization. - Tobias Sutter; Bart P. G. Van Parys; Daniel Kuhn 001 (Oper. Res., 2024)

A Two-Timescale Primal-Dual Framework for Reinforcement Learning via Online Dual Variable Guidance. - Axel Friedrich Wolter; Tobias Sutter (CoRR, 2025)

Reliability-Adjusted Prioritized Experience Replay. - Leonard S. Pleiss; Tobias Sutter; Maximilian Schiffer (CoRR, 2025)

Computing Optimal Joint Chance Constrained Control Policies. - Niklas Schmid; Marta Fochesato; Sarah H. Q. Li; Tobias Sutter; John Lygeros (IEEE Trans. Autom. Control., 2025)