| 2025 | ICML | Solving Probabilistic Verification Problems of Neural Networks using Branch and Bound. | David Boetius, Stefan Leue, Tobias Sutter |
| 2024 | ICML | Regularized Q-learning through Robust Averaging. | Peter Schmitt-Frster, Tobias Sutter |
| 2023 | ICLR | ISAAC Newton: Input-based Approximate Curvature for Newton's Method. | Felix Petersen, Tobias Sutter, Christian Borgelt, Dongsung Huh, Hilde Kuehne, Yuekai Sun, Oliver Deussen |
| 2023 | ICML | A Robust Optimisation Perspective on Counterexample-Guided Repair of Neural Networks. | David Boetius, Stefan Leue, Tobias Sutter |
| 2023 | ICML | End-to-End Learning for Stochastic Optimization: A Bayesian Perspective. | Yves Rychener, Daniel Kuhn, Tobias Sutter |
| 2021 | ICML | Distributionally Robust Optimization with Markovian Data. | Mengmeng Li, Tobias Sutter, Daniel Kuhn |
| 2014 | ISIT | Efficient approximation of discrete memoryless channel capacities. | David Sutter, Peyman Mohajerin Esfahani, Tobias Sutter, John Lygeros |
| 2014 | ISIT | Capacity approximation of memoryless channels with countable output alphabets. | Tobias Sutter, Peyman Mohajerin Esfahani, David Sutter, John Lygeros |