| 2025 | AISTATS | From Learning to Optimize to Learning Optimization Algorithms. | Camille Castera, Peter Ochs |
| 2025 | ICML | Automatic Differentiation of Optimization Algorithms with Time-Varying Updates. | Sheheryar Mehmood, Peter Ochs |
| 2025 | ICML | A Generalization Result for Convergence in Learning-to-Optimize. | Michael Sucker, Peter Ochs |
| 2023 | AISTATS | PAC-Bayesian Learning of Optimization Algorithms. | Michael Sucker, Peter Ochs |
| 2021 | AISTATS | Differentiating the Value Function by using Convex Duality. | Sheheryar Mehmood, Peter Ochs |
| 2020 | ACCV | Self-supervised Sparse to Dense Motion Segmentation. | Amirhossein Kardoost, Kalun Ho, Peter Ochs, Margret Keuper |
| 2020 | AISTATS | Automatic Differentiation of Some First-Order Methods in Parametric Optimization. | Sheheryar Mehmood, Peter Ochs |
| 2019 | ICML | Model Function Based Conditional Gradient Method with Armijo-like Line Search. | Peter Ochs, Yura Malitsky |
| 2018 | ECCV | Lifting Layers: Analysis and Applications. | Peter Ochs, Tim Meinhardt, Laura Leal-Taix, Michael Mller |
| 2013 | CVPR | An Iterated L1 Algorithm for Non-smooth Non-convex Optimization in Computer Vision. | Peter Ochs, Alexey Dosovitskiy, Thomas Brox, Thomas Pock |
| 2012 | CVPR | Higher order motion models and spectral clustering. | Peter Ochs, Thomas Brox |
| 2011 | ICCV | Object segmentation in video: A hierarchical variational approach for turning point trajectories into dense regions. | Peter Ochs, Thomas Brox |