Peter Ochs - RankMe

60 papers A* 7A 4Journal 39Unranked 9

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Near-optimal Closed-loop Method via Lyapunov Damping. J. Optim. Theory Appl. Severin Maier, Camille Castera, Peter Ochs
2026	J	`jnl`	Understanding the Curse of Unrolling. CoRR Sheheryar Mehmood, Florian Knoll, Peter Ochs
2025	A*	`conf`	A Generalization Result for Convergence in Learning-to-Optimize. ICML Michael Sucker, Peter Ochs
2025	A*	`conf`	Automatic Differentiation of Optimization Algorithms with Time-Varying Updates. ICML Sheheryar Mehmood, Peter Ochs
2025	A	`conf`	From Learning to Optimize to Learning Optimization Algorithms. AISTATS Camille Castera, Peter Ochs
2025	J	`jnl`	Learning-to-Optimize with PAC-Bayesian Guarantees: Theoretical Considerations and Practical Implementation. J. Mach. Learn. Res. Michael Sucker, Jalal Fadili, Peter Ochs
2025	J	`jnl`	Quasi-Newton Methods for Monotone Inclusions: Efficient Resolvent Calculus and Primal-Dual Algorithms. SIAM J. Imaging Sci. Shida Wang, Jalal Fadili, Peter Ochs
2025	J	`jnl`	Symmetries in PAC-Bayesian Learning. CoRR Armin Beck, Peter Ochs
2024	J	`jnl`	A Generalization Result for Convergence in Learning-to-Optimize. CoRR Michael Sucker, Peter Ochs
2024	J	`jnl`	A Markovian Model for Learning-to-Optimize. CoRR Michael Sucker, Peter Ochs
2024	J	`jnl`	Automatic Differentiation of Optimization Algorithms with Time-Varying Updates. CoRR Sheheryar Mehmood, Peter Ochs
2024	J	`jnl`	Continuous Newton-like Methods Featuring Inertia and Variable Mass. SIAM J. Optim. Camille Castera, Hédy Attouch, Jalal Fadili, Peter Ochs
2024	J	`jnl`	From Learning to Optimize to Learning Optimization Algorithms. CoRR Camille Castera, Peter Ochs
2024	J	`jnl`	Learning-to-Optimize with PAC-Bayesian Guarantees: Theoretical Considerations and Practical Implementation. CoRR Michael Sucker, Jalal Fadili, Peter Ochs
2023	—	`conf`	A Quasi-Newton Primal-Dual Algorithm with Line Search. SSVM Shida Wang, Jalal Fadili, Peter Ochs
2023	J	`jnl`	An abstract convergence framework with application to inertial inexact forward-backward methods. Comput. Optim. Appl. Silvia Bonettini, Peter Ochs, Marco Prato, Simone Rebegoldi
2023	J	`jnl`	An abstract convergence framework with application to inertial inexact forward-backward methods. CoRR Silvia Bonettini, Peter Ochs, Marco Prato, Simone Rebegoldi
2023	J	`jnl`	Near-optimal Closed-loop Method via Lyapunov Damping for Convex Optimization. CoRR Severin Maier, Camille Castera, Peter Ochs
2023	A	`conf`	PAC-Bayesian Learning of Optimization Algorithms. AISTATS Michael Sucker, Peter Ochs
2022	J	`jnl`	Fixed-Point Automatic Differentiation of Forward-Backward Splitting Algorithms for Partly Smooth Functions. CoRR Sheheryar Mehmood, Peter Ochs
2022	J	`jnl`	Global convergence of model function based Bregman proximal minimization algorithms. J. Glob. Optim. Mahesh Chandra Mukkamala, Jalal Fadili, Peter Ochs
2022	J	`jnl`	PAC-Bayesian Learning of Optimization Algorithms. CoRR Michael Sucker, Peter Ochs
2021	—	`conf`	Bregman Proximal Gradient Algorithms for Deep Matrix Factorization. SSVM Mahesh Chandra Mukkamala, Felix Westerkamp, Emanuel Laude, Daniel Cremers, Peter Ochs
2021	A	`conf`	Differentiating the Value Function by using Convex Duality. AISTATS Sheheryar Mehmood, Peter Ochs
2020	A	`conf`	Automatic Differentiation of Some First-Order Methods in Parametric Optimization. AISTATS Sheheryar Mehmood, Peter Ochs
2020	J	`jnl`	Bregman Proximal Mappings and Bregman-Moreau Envelopes Under Relative Prox-Regularity. J. Optim. Theory Appl. Emanuel Laude, Peter Ochs, Daniel Cremers
2020	J	`jnl`	Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Nonconvex Optimization. SIAM J. Math. Data Sci. Mahesh Chandra Mukkamala, Peter Ochs, Thomas Pock, Shoham Sabach
2020	J	`jnl`	Global Convergence of Model Function Based Bregman Proximal Minimization Algorithms. CoRR Mahesh Chandra Mukkamala, Jalal Fadili, Peter Ochs
2020	—	`conf`	Self-supervised Sparse to Dense Motion Segmentation. ACCV (2) Amirhossein Kardoost, Kalun Ho, Peter Ochs, Margret Keuper
2020	J	`jnl`	Self-supervised Sparse to Dense Motion Segmentation. CoRR Amirhossein Kardoost, Kalun Ho, Peter Ochs, Margret Keuper
2019	J	`jnl`	Adaptive FISTA for Nonconvex Optimization. SIAM J. Optim. Peter Ochs, Thomas Pock
2019	A*	`conf`	Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms. NeurIPS Mahesh Chandra Mukkamala, Peter Ochs
2019	J	`jnl`	Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms. CoRR Mahesh Chandra Mukkamala, Peter Ochs
2019	J	`jnl`	Bregman Proximal Framework for Deep Linear Neural Networks. CoRR Mahesh Chandra Mukkamala, Felix Westerkamp, Emanuel Laude, Daniel Cremers, Peter Ochs
2019	J	`jnl`	Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Non-Convex Optimization. CoRR Mahesh Chandra Mukkamala, Peter Ochs, Thomas Pock, Shoham Sabach
2019	A*	`conf`	Model Function Based Conditional Gradient Method with Armijo-like Line Search. ICML Peter Ochs, Yura Malitsky
2019	J	`jnl`	Model Function Based Conditional Gradient Method with Armijo-like Line Search. CoRR Yura Malitsky, Peter Ochs
2019	J	`jnl`	Non-smooth Non-convex Bregman Minimization: Unification and New Algorithms. J. Optim. Theory Appl. Peter Ochs, Jalal Fadili, Thomas Brox
2019	J	`jnl`	On Quasi-Newton Forward-Backward Splitting: Proximal Calculus and Convergence. SIAM J. Optim. Stephen Becker, Jalal Fadili, Peter Ochs
2019	J	`jnl`	Unifying Abstract Inexact Convergence Theorems and Block Coordinate Variable Metric iPiano. SIAM J. Optim. Peter Ochs
2018	—	`conf`	AFSI: Adaptive Restart for Fast Semi-Iterative Schemes for Convex Optimisation. GCPR Jón Arnar Tómasson, Peter Ochs, Joachim Weickert
2018	—	`conf`	Lifting Layers: Analysis and Applications. ECCV (1) Peter Ochs, Tim Meinhardt, Laura Leal-Taixé, Michael Möller
2018	J	`jnl`	Lifting Layers: Analysis and Applications. CoRR Peter Ochs, Tim Meinhardt, Laura Leal-Taixé, Michael Möller
2018	J	`jnl`	Local Convergence of the Heavy-Ball Method and iPiano for Non-convex Optimization. J. Optim. Theory Appl. Peter Ochs
2017	J	`jnl`	Non-smooth Non-convex Bregman Minimization: Unification and new Algorithms. CoRR Peter Ochs, Jalal Fadili, Thomas Brox
2016	—	`conf`	FSI Schemes: Fast Semi-Iterative Solvers for PDEs and Optimisation Methods. GCPR David Hafner, Peter Ochs, Joachim Weickert, Martin Reißel, Sven Grewenig
2016	—	`conf`	Robust Interactive Multi-label Segmentation with an Advanced Edge Detector. GCPR Sabine Müller, Peter Ochs, Joachim Weickert, Norbert M. Graf
2016	J	`jnl`	Techniques for Gradient-Based Bilevel Optimization with Non-smooth Lower Level Problems. J. Math. Imaging Vis. Peter Ochs, René Ranftl, Thomas Brox, Thomas Pock
2015	—	`conf`	Bilevel Optimization with Nonsmooth Lower Level Problems. SSVM Peter Ochs, René Ranftl, Thomas Brox, Thomas Pock
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	J	`jnl`	On Iteratively Reweighted Algorithms for Nonsmooth Nonconvex Optimization in Computer Vision. SIAM J. Imaging Sci. Peter Ochs, Alexey Dosovitskiy, Thomas Brox, Thomas Pock
2015	J	`jnl`	iPiasco: Inertial Proximal Algorithm for Strongly Convex Optimization. J. Math. Imaging Vis. Peter Ochs, Thomas Brox, Thomas Pock
2014	J	`jnl`	Segmentation of Moving Objects by Long Term Video Analysis. IEEE Trans. Pattern Anal. Mach. Intell. Peter Ochs, Jitendra Malik, Thomas Brox
2014	J	`jnl`	iPiano: Inertial Proximal Algorithm for Non-Convex Optimization. CoRR Peter Ochs, Yunjin Chen, Thomas Brox, Thomas Pock
2014	J	`jnl`	iPiano: Inertial Proximal Algorithm for Nonconvex Optimization. SIAM J. Imaging Sci. Peter Ochs, Yunjin Chen, Thomas Brox, Thomas Pock
2013	A*	`conf`	An Iterated L1 Algorithm for Non-smooth Non-convex Optimization in Computer Vision. CVPR Peter Ochs, Alexey Dosovitskiy, Thomas Brox, Thomas Pock
2012	—	`conf`	Hierarchy of Localized Random Forests for Video Annotation. DAGM/OAGM Symposium Naveen Shankar Nagaraja, Peter Ochs, Kun Liu, Thomas Brox
2012	A*	`conf`	Higher order motion models and spectral clustering. CVPR Peter Ochs, Thomas Brox
2012	J	`jnl`	Multiview Deblurring for 3-D Images from Light-Sheet-Based Fluorescence Microscopy. IEEE Trans. Image Process. Maja Temerinac-Ott, Olaf Ronneberger, Peter Ochs, Wolfgang Driever, Thomas Brox, Hans Burkhardt
2011	A*	`conf`	Object segmentation in video: A hierarchical variational approach for turning point trajectories into dense regions. ICCV Peter Ochs, Thomas Brox