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Peter Ochs
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- affiliation: Saarland University, Department of Mathematics, Germany
- affiliation: University of Freiburg, Department of Computer Science, Germany
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2020 – today
- 2024
- [j16]Camille Castera
, Hédy Attouch, Jalal Fadili, Peter Ochs:
Continuous Newton-like Methods Featuring Inertia and Variable Mass. SIAM J. Optim. 34(1): 251-277 (2024) - [i18]Michael Sucker, Jalal Fadili, Peter Ochs:
Learning-to-Optimize with PAC-Bayesian Guarantees: Theoretical Considerations and Practical Implementation. CoRR abs/2404.03290 (2024) - [i17]Camille Castera, Peter Ochs:
From Learning to Optimize to Learning Optimization Algorithms. CoRR abs/2405.18222 (2024) - [i16]Michael Sucker, Peter Ochs:
A Markovian Model for Learning-to-Optimize. CoRR abs/2408.11629 (2024) - [i15]Michael Sucker, Peter Ochs:
A Generalization Result for Convergence in Learning-to-Optimize. CoRR abs/2410.07704 (2024) - [i14]Sheheryar Mehmood, Peter Ochs:
Automatic Differentiation of Optimization Algorithms with Time-Varying Updates. CoRR abs/2410.15923 (2024) - 2023
- [j15]Silvia Bonettini
An abstract convergence framework with application to inertial inexact forward-backward methods. Comput. Optim. Appl. 84(2): 319-362 (2023) - [c17]Michael Sucker, Peter Ochs:
PAC-Bayesian Learning of Optimization Algorithms. AISTATS 2023: 8145-8164 - [c16]Shida Wang
A Quasi-Newton Primal-Dual Algorithm with Line Search. SSVM 2023: 444-456 - [i13]Silvia Bonettini, Peter Ochs, Marco Prato, Simone Rebegoldi:
An abstract convergence framework with application to inertial inexact forward-backward methods. CoRR abs/2302.07545 (2023) - [i12]Severin Maier, Camille Castera, Peter Ochs:
Near-optimal Closed-loop Method via Lyapunov Damping for Convex Optimization. CoRR abs/2311.10053 (2023) - 2022
- [j14]Mahesh Chandra Mukkamala, Jalal Fadili, Peter Ochs:
Global convergence of model function based Bregman proximal minimization algorithms. J. Glob. Optim. 83(4): 753-781 (2022) - [i11]Sheheryar Mehmood, Peter Ochs:
Fixed-Point Automatic Differentiation of Forward-Backward Splitting Algorithms for Partly Smooth Functions. CoRR abs/2208.03107 (2022) - [i10]Michael Sucker, Peter Ochs:
PAC-Bayesian Learning of Optimization Algorithms. CoRR abs/2210.11113 (2022) - 2021
- [c15]Sheheryar Mehmood, Peter Ochs:
Differentiating the Value Function by using Convex Duality. AISTATS 2021: 3871-3879 - [c14]Mahesh Chandra Mukkamala, Felix Westerkamp, Emanuel Laude, Daniel Cremers
Bregman Proximal Gradient Algorithms for Deep Matrix Factorization. SSVM 2021: 204-215 - 2020
- [j13]Emanuel Laude
Bregman Proximal Mappings and Bregman-Moreau Envelopes Under Relative Prox-Regularity. J. Optim. Theory Appl. 184(3): 724-761 (2020) - [j12]Mahesh Chandra Mukkamala
Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Nonconvex Optimization. SIAM J. Math. Data Sci. 2(3): 658-682 (2020) - [c13]Amirhossein Kardoost
Self-supervised Sparse to Dense Motion Segmentation. ACCV (2) 2020: 421-437 - [c12]Sheheryar Mehmood, Peter Ochs:
Automatic Differentiation of Some First-Order Methods in Parametric Optimization. AISTATS 2020: 1584-1594 - [i9]Amirhossein Kardoost, Kalun Ho, Peter Ochs, Margret Keuper:
Self-supervised Sparse to Dense Motion Segmentation. CoRR abs/2008.07872 (2020) - [i8]Mahesh Chandra Mukkamala, Jalal Fadili, Peter Ochs:
Global Convergence of Model Function Based Bregman Proximal Minimization Algorithms. CoRR abs/2012.13161 (2020)
2010 – 2019
- 2019
- [j11]Peter Ochs
Non-smooth Non-convex Bregman Minimization: Unification and New Algorithms. J. Optim. Theory Appl. 181(1): 244-278 (2019) - [j10]Peter Ochs:
Unifying Abstract Inexact Convergence Theorems and Block Coordinate Variable Metric iPiano. SIAM J. Optim. 29(1): 541-570 (2019) - [j9]Stephen Becker
On Quasi-Newton Forward-Backward Splitting: Proximal Calculus and Convergence. SIAM J. Optim. 29(4): 2445-2481 (2019) - [j8]Peter Ochs, Thomas Pock:
Adaptive FISTA for Nonconvex Optimization. SIAM J. Optim. 29(4): 2482-2503 (2019) - [c11]Peter Ochs, Yura Malitsky:
Model Function Based Conditional Gradient Method with Armijo-like Line Search. ICML 2019: 4891-4900 - [c10]Mahesh Chandra Mukkamala, Peter Ochs:
Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms. NeurIPS 2019: 4268-4278 - [i7]Yura Malitsky, Peter Ochs:
Model Function Based Conditional Gradient Method with Armijo-like Line Search. CoRR abs/1901.08087 (2019) - [i6]Mahesh Chandra Mukkamala, Peter Ochs, Thomas Pock, Shoham Sabach:
Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Non-Convex Optimization. CoRR abs/1904.03537 (2019) - [i5]Mahesh Chandra Mukkamala, Peter Ochs:
Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms. CoRR abs/1905.09050 (2019) - [i4]Mahesh Chandra Mukkamala, Felix Westerkamp, Emanuel Laude, Daniel Cremers, Peter Ochs:
Bregman Proximal Framework for Deep Linear Neural Networks. CoRR abs/1910.03638 (2019) - 2018
- [j7]Peter Ochs
Local Convergence of the Heavy-Ball Method and iPiano for Non-convex Optimization. J. Optim. Theory Appl. 177(1): 153-180 (2018) - [c9]Jón Arnar Tómasson, Peter Ochs, Joachim Weickert:
AFSI: Adaptive Restart for Fast Semi-Iterative Schemes for Convex Optimisation. GCPR 2018: 669-681 - [c8]Peter Ochs, Tim Meinhardt, Laura Leal-Taixé, Michael Möller:
Lifting Layers: Analysis and Applications. ECCV (1) 2018: 53-68 - [i3]Peter Ochs, Tim Meinhardt, Laura Leal-Taixé, Michael Möller:
Lifting Layers: Analysis and Applications. CoRR abs/1803.08660 (2018) - 2017
- [i2]Peter Ochs, Jalal Fadili, Thomas Brox:
Non-smooth Non-convex Bregman Minimization: Unification and new Algorithms. CoRR abs/1707.02278 (2017) - 2016
- [j6]Peter Ochs, René Ranftl, Thomas Brox, Thomas Pock:
Techniques for Gradient-Based Bilevel Optimization with Non-smooth Lower Level Problems. J. Math. Imaging Vis. 56(2): 175-194 (2016) - [c7]David Hafner, Peter Ochs, Joachim Weickert, Martin Reißel, Sven Grewenig:
FSI Schemes: Fast Semi-Iterative Solvers for PDEs and Optimisation Methods. GCPR 2016: 91-102 - [c6]Sabine Müller, Peter Ochs, Joachim Weickert, Norbert M. Graf
Robust Interactive Multi-label Segmentation with an Advanced Edge Detector. GCPR 2016: 117-128 - 2015
- [b1]Peter Ochs:
Long term motion analysis for object level grouping and nonsmooth optimization methods = Langzeitanalyse von Bewegungen zur objektorientierten Gruppierung und nichglatte Optimierungsmethoden. University of Freiburg, Germany, 2015 - [j5]Peter Ochs, Thomas Brox, Thomas Pock:
iPiasco: Inertial Proximal Algorithm for Strongly Convex Optimization. J. Math. Imaging Vis. 53(2): 171-181 (2015) - [j4]Peter Ochs, Alexey Dosovitskiy, Thomas Brox, Thomas Pock:
On Iteratively Reweighted Algorithms for Nonsmooth Nonconvex Optimization in Computer Vision. SIAM J. Imaging Sci. 8(1): 331-372 (2015) - [c5]Peter Ochs, René Ranftl, Thomas Brox, Thomas Pock:
Bilevel Optimization with Nonsmooth Lower Level Problems. SSVM 2015: 654-665 - 2014
- [j3]Peter Ochs, Jitendra Malik, Thomas Brox:
Segmentation of Moving Objects by Long Term Video Analysis. IEEE Trans. Pattern Anal. Mach. Intell. 36(6): 1187-1200 (2014) - [j2]Peter Ochs, Yunjin Chen, Thomas Brox, Thomas Pock:
iPiano: Inertial Proximal Algorithm for Nonconvex Optimization. SIAM J. Imaging Sci. 7(2): 1388-1419 (2014) - [i1]Peter Ochs, Yunjin Chen, Thomas Brox, Thomas Pock:
iPiano: Inertial Proximal Algorithm for Non-Convex Optimization. CoRR abs/1404.4805 (2014) - 2013
- [c4]Peter Ochs, Alexey Dosovitskiy, Thomas Brox, Thomas Pock:
An Iterated L1 Algorithm for Non-smooth Non-convex Optimization in Computer Vision. CVPR 2013: 1759-1766 - 2012
- [j1]Maja Temerinac-Ott
Multiview Deblurring for 3-D Images from Light-Sheet-Based Fluorescence Microscopy. IEEE Trans. Image Process. 21(4): 1863-1873 (2012) - [c3]Peter Ochs, Thomas Brox:
Higher order motion models and spectral clustering. CVPR 2012: 614-621 - [c2]Naveen Shankar Nagaraja, Peter Ochs, Kun Liu, Thomas Brox:
Hierarchy of Localized Random Forests for Video Annotation. DAGM/OAGM Symposium 2012: 21-30 - 2011
- [c1]Peter Ochs, Thomas Brox:
Object segmentation in video: A hierarchical variational approach for turning point trajectories into dense regions. ICCV 2011: 1583-1590
Coauthor Index
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