Tobias Sutter

According to our database, Tobias Sutter authored at least 31 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

A Pareto Dominance Principle for Data-Driven Optimization.

Tobias Sutter, Bart P. G. Van Parys, Daniel Kuhn

Oper. Res., 2024

Joint Chance Constrained Optimal Control via Linear Programming.

Niklas Schmid, Marta Fochesato, Tobias Sutter, John Lygeros

IEEE Control. Syst. Lett., 2024

Probabilistic Verification of Neural Networks using Branch and Bound.

David Boetius, Stefan Leue, Tobias Sutter

CoRR, 2024

Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces.

Angeliki Kamoutsi, Peter Schmitt-Förster, Tobias Sutter, Volkan Cevher, John Lygeros

CoRR, 2024

Finding the DeepDream for Time Series: Activation Maximization for Univariate Time Series.

Udo Schlegel, Daniel A. Keim, Tobias Sutter

Proceedings of the Workshop on Explainable AI for Time Series and Data Streams (TempXAI 2024) co-located with The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2024), 2024

Regularized Q-learning through Robust Averaging.

Peter Schmitt-Förster, Tobias Sutter

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Efficient Learning of a Linear Dynamical System With Stability Guarantees.

Wouter Jongeneel, Tobias Sutter, Daniel Kuhn

IEEE Trans. Autom. Control., May, 2023

Computing Optimal Joint Chance Constrained Control Policies.

Niklas Schmid, Marta Fochesato, Sarah H. Q. Li, Tobias Sutter, John Lygeros

CoRR, 2023

Policy Gradient Algorithms for Robust MDPs with Non-Rectangular Uncertainty Sets.

Mengmeng Li, Tobias Sutter, Daniel Kuhn

CoRR, 2023

End-to-End Learning for Stochastic Optimization: A Bayesian Perspective.

Yves Rychener, Daniel Kuhn, Tobias Sutter

Proceedings of the International Conference on Machine Learning, 2023

A Robust Optimisation Perspective on Counterexample-Guided Repair of Neural Networks.

David Boetius, Stefan Leue, Tobias Sutter

Proceedings of the International Conference on Machine Learning, 2023

ISAAC Newton: Input-based Approximate Curvature for Newton's Method.

Felix Petersen, Tobias Sutter, Christian Borgelt, Dongsung Huh, Hilde Kuehne, Yuekai Sun, Oliver Deussen

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Topological Linear System Identification via Moderate Deviations Theory.

Wouter Jongeneel, Tobias Sutter, Daniel Kuhn

IEEE Control. Syst. Lett., 2022

2021

Robust Generalization despite Distribution Shift via Minimum Discriminating Information.

Tobias Sutter, Andreas Krause, Daniel Kuhn

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Distributionally Robust Optimization with Markovian Data.

Mengmeng Li, Tobias Sutter, Daniel Kuhn

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Quantum Legendre-Fenchel Transform.

David Sutter, Giacomo Nannicini, Tobias Sutter, Stefan Woerner

CoRR, 2020

2019

Generalized Maximum Entropy Estimation.

Tobias Sutter, David Sutter, Peyman Mohajerin Esfahani, John Lygeros

J. Mach. Learn. Res., 2019

2018

From Infinite to Finite Programs: Explicit Error Bounds with Applications to Approximate Dynamic Programming.

Peyman Mohajerin Esfahani, Tobias Sutter, Daniel Kuhn, John Lygeros

SIAM J. Optim., 2018

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

Convex programming in optimal control and information theory.

Tobias Sutter

CoRR, 2017

Capacity of random channels with large alphabets.

Tobias Sutter, David Sutter, John Lygeros

Adv. Math. Commun., 2017

Data-driven approximate dynamic programming: A linear programming approach.

Tobias Sutter, Angeliki Kamoutsi, Peyman Mohajerin Esfahani, John Lygeros

Proceedings of the 56th IEEE Annual Conference on Decision and Control, 2017

2016

Efficient Approximation of Quantum Channel Capacities.

David Sutter, Tobias Sutter, Peyman Mohajerin Esfahani, Renato Renner

IEEE Trans. Inf. Theory, 2016

A Variational Approach to Path Estimation and Parameter Inference of Hidden Diffusion Processes.

Tobias Sutter, Arnab Ganguly, Heinz Koeppl

J. Mach. Learn. Res., 2016

2015

Efficient Approximation of Channel Capacities.

Tobias Sutter, David Sutter, Peyman Mohajerin Esfahani, John Lygeros

IEEE Trans. Inf. Theory, 2015

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

2014

Capacity approximation of memoryless channels with countable output alphabets.

Tobias Sutter, Peyman Mohajerin Esfahani, David Sutter, John Lygeros

Proceedings of the 2014 IEEE International Symposium on Information Theory, Honolulu, HI, USA, June 29, 2014

Efficient approximation of discrete memoryless channel capacities.

David Sutter, Peyman Mohajerin Esfahani, Tobias Sutter, John Lygeros

Proceedings of the 2014 IEEE International Symposium on Information Theory, Honolulu, HI, USA, June 29, 2014

Approximation of constrained average cost Marks.

Tobias Sutter, Peyman Mohajerin Esfahani, John Lygeros

Proceedings of the 53rd IEEE Conference on Decision and Control, 2014

Asymptotic capacity of a random channel.

Tobias Sutter, David Sutter, John Lygeros

Proceedings of the 52nd Annual Allerton Conference on Communication, 2014

2013

Isospectral flows on a class of finite-dimensional Jacobi matrices.

Tobias Sutter, Debasish Chatterjee, Federico Ramponi, John Lygeros

Syst. Control. Lett., 2013