Object segmentation in video: A hierarchical variational approach for turning point trajectories into dense regions. - Peter Ochs; Thomas Brox (ICCV, 2011)

Higher order motion models and spectral clustering. - Peter Ochs; Thomas Brox (CVPR, 2012)

Hierarchy of Localized Random Forests for Video Annotation. - Naveen Shankar Nagaraja; Peter Ochs; Kun Liu 003; Thomas Brox (DAGM/OAGM Symposium, 2012)

Multiview Deblurring for 3-D Images from Light-Sheet-Based Fluorescence Microscopy. - Maja Temerinac-Ott; Olaf Ronneberger; Peter Ochs; Wolfgang Driever; Thomas Brox; Hans Burkhardt (IEEE Trans. Image Process., 2012)

An Iterated L1 Algorithm for Non-smooth Non-convex Optimization in Computer Vision. - Peter Ochs; Alexey Dosovitskiy; Thomas Brox; Thomas Pock (CVPR, 2013)

iPiano: Inertial Proximal Algorithm for Non-Convex Optimization. - Peter Ochs; Yunjin Chen; Thomas Brox; Thomas Pock (CoRR, 2014)

Segmentation of Moving Objects by Long Term Video Analysis. - Peter Ochs; Jitendra Malik; Thomas Brox (IEEE Trans. Pattern Anal. Mach. Intell., 2014)

iPiano: Inertial Proximal Algorithm for Nonconvex Optimization. - Peter Ochs; Yunjin Chen; Thomas Brox; Thomas Pock (SIAM J. Imaging Sci., 2014)

Bilevel Optimization with Nonsmooth Lower Level Problems. - Peter Ochs; René Ranftl; Thomas Brox; Thomas Pock (SSVM, 2015)

iPiasco: Inertial Proximal Algorithm for Strongly Convex Optimization. - Peter Ochs; Thomas Brox; Thomas Pock (J. Math. Imaging Vis., 2015)

On Iteratively Reweighted Algorithms for Nonsmooth Nonconvex Optimization in Computer Vision. - Peter Ochs; Alexey Dosovitskiy; Thomas Brox; Thomas Pock (SIAM J. Imaging Sci., 2015)

Long term motion analysis for object level grouping and nonsmooth optimization methods = Langzeitanalyse von Bewegungen zur objektorientierten Gruppierung und nichglatte Optimierungsmethoden. - Peter Ochs (2015)

FSI Schemes: Fast Semi-Iterative Solvers for PDEs and Optimisation Methods. - David Hafner; Peter Ochs; Joachim Weickert; Martin Reißel; Sven Grewenig (GCPR, 2016)

Robust Interactive Multi-label Segmentation with an Advanced Edge Detector. - Sabine Müller 001; Peter Ochs; Joachim Weickert; Norbert M. Graf (GCPR, 2016)

Techniques for Gradient-Based Bilevel Optimization with Non-smooth Lower Level Problems. - Peter Ochs; René Ranftl; Thomas Brox; Thomas Pock (J. Math. Imaging Vis., 2016)

AFSI: Adaptive Restart for Fast Semi-Iterative Schemes for Convex Optimisation. - Jón Arnar Tómasson; Peter Ochs; Joachim Weickert (GCPR, 2018)

Lifting Layers: Analysis and Applications. - Peter Ochs; Tim Meinhardt; Laura Leal-Taixé; Michael Möller 001 (ECCV (1), 2018)

Local Convergence of the Heavy-Ball Method and iPiano for Non-convex Optimization. - Peter Ochs (J. Optim. Theory Appl., 2018)

Model Function Based Conditional Gradient Method with Armijo-like Line Search. - Peter Ochs; Yura Malitsky (ICML, 2019)

Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms. - Mahesh Chandra Mukkamala; Peter Ochs (NeurIPS, 2019)

Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Non-Convex Optimization. - Mahesh Chandra Mukkamala; Peter Ochs; Thomas Pock; Shoham Sabach (CoRR, 2019)

Bregman Proximal Framework for Deep Linear Neural Networks. - Mahesh Chandra Mukkamala; Felix Westerkamp; Emanuel Laude; Daniel Cremers; Peter Ochs (CoRR, 2019)

Non-smooth Non-convex Bregman Minimization: Unification and New Algorithms. - Peter Ochs; Jalal Fadili; Thomas Brox (J. Optim. Theory Appl., 2019)

On Quasi-Newton Forward-Backward Splitting: Proximal Calculus and Convergence. - Stephen Becker; Jalal Fadili; Peter Ochs (SIAM J. Optim., 2019)

Unifying Abstract Inexact Convergence Theorems and Block Coordinate Variable Metric iPiano. - Peter Ochs (SIAM J. Optim., 2019)

Adaptive FISTA for Nonconvex Optimization. - Peter Ochs; Thomas Pock (SIAM J. Optim., 2019)

Self-supervised Sparse to Dense Motion Segmentation. - Amirhossein Kardoost; Kalun Ho; Peter Ochs; Margret Keuper (ACCV (2), 2020)

Automatic Differentiation of Some First-Order Methods in Parametric Optimization. - Sheheryar Mehmood; Peter Ochs (AISTATS, 2020)

Bregman Proximal Mappings and Bregman-Moreau Envelopes Under Relative Prox-Regularity. - Emanuel Laude; Peter Ochs; Daniel Cremers (J. Optim. Theory Appl., 2020)

Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Nonconvex Optimization. - Mahesh Chandra Mukkamala; Peter Ochs; Thomas Pock; Shoham Sabach (SIAM J. Math. Data Sci., 2020)

Differentiating the Value Function by using Convex Duality. - Sheheryar Mehmood; Peter Ochs (AISTATS, 2021)

Bregman Proximal Gradient Algorithms for Deep Matrix Factorization. - Mahesh Chandra Mukkamala; Felix Westerkamp; Emanuel Laude; Daniel Cremers; Peter Ochs (SSVM, 2021)

Fixed-Point Automatic Differentiation of Forward-Backward Splitting Algorithms for Partly Smooth Functions. - Sheheryar Mehmood; Peter Ochs (CoRR, 2022)

Global convergence of model function based Bregman proximal minimization algorithms. - Mahesh Chandra Mukkamala; Jalal Fadili; Peter Ochs (J. Glob. Optim., 2022)

PAC-Bayesian Learning of Optimization Algorithms. - Michael Sucker; Peter Ochs (AISTATS, 2023)

A Quasi-Newton Primal-Dual Algorithm with Line Search. - Shida Wang; Jalal Fadili; Peter Ochs (SSVM, 2023)

Near-optimal Closed-loop Method via Lyapunov Damping for Convex Optimization. - Severin Maier; Camille Castera; Peter Ochs (CoRR, 2023)

An abstract convergence framework with application to inertial inexact forward-backward methods. - Silvia Bonettini; Peter Ochs; Marco Prato; Simone Rebegoldi (Comput. Optim. Appl., 2023)

Learning-to-Optimize with PAC-Bayesian Guarantees: Theoretical Considerations and Practical Implementation. - Michael Sucker; Jalal Fadili; Peter Ochs (CoRR, 2024)

A Markovian Model for Learning-to-Optimize. - Michael Sucker; Peter Ochs (CoRR, 2024)

A Generalization Result for Convergence in Learning-to-Optimize. - Michael Sucker; Peter Ochs (CoRR, 2024)

Automatic Differentiation of Optimization Algorithms with Time-Varying Updates. - Sheheryar Mehmood; Peter Ochs (CoRR, 2024)

Continuous Newton-like Methods Featuring Inertia and Variable Mass. - Camille Castera; Hédy Attouch; Jalal Fadili; Peter Ochs (SIAM J. Optim., 2024)

From Learning to Optimize to Learning Optimization Algorithms. - Camille Castera; Peter Ochs (AISTATS, 2025)

Quasi-Newton Methods for Monotone Inclusions: Efficient Resolvent Calculus and Primal-Dual Algorithms. - Shida Wang; Jalal Fadili; Peter Ochs (SIAM J. Imaging Sci., 2025)