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ORCIDShoham Sabach
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2020 – today
- 2024
[j31]Eyal Cohen, D. Russell Luke, Titus Pinta
, Shoham Sabach, Marc Teboulle:
A semi-Bregman proximal alternating method for a class of nonconvex problems: local and global convergence analysis. J. Glob. Optim. 89(1): 33-55 (2024)- [j30]Noga Ram, Shoham Sabach:
A Globally Convergent Inertial First-Order Optimization Method for Multidimensional Scaling. J. Optim. Theory Appl. 202(2): 949-974 (2024) - [c8]Ruichen Jiang, Parameswaran Raman, Shoham Sabach, Aryan Mokhtari, Mingyi Hong, Volkan Cevher:
Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate. AISTATS 2024: 4411-4419 - [c7]Zuxin Liu, Jesse Zhang, Kavosh Asadi, Yao Liu, Ding Zhao, Shoham Sabach, Rasool Fakoor:
TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models. ICLR 2024 - [c6]Kavosh Asadi, Yao Liu, Shoham Sabach, Ming Yin, Rasool Fakoor:
Learning the Target Network in Function Space. ICML 2024 - [c5]Kaan Ozkara, Can Karakus, Parameswaran Raman, Mingyi Hong, Shoham Sabach, Branislav Kveton, Volkan Cevher:
MADA: Meta-Adaptive Optimizers Through Hyper-Gradient Descent. ICML 2024 - [i14]Ruichen Jiang, Parameswaran Raman, Shoham Sabach, Aryan Mokhtari, Mingyi Hong, Volkan Cevher:
Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate. CoRR abs/2401.03058 (2024) - [i13]Kaan Ozkara, Can Karakus, Parameswaran Raman, Mingyi Hong, Shoham Sabach, Branislav Kveton, Volkan Cevher:
MADA: Meta-Adaptive Optimizers through hyper-gradient Descent. CoRR abs/2401.08893 (2024) - [i12]Kavosh Asadi, Yao Liu, Shoham Sabach, Ming Yin, Rasool Fakoor:
Learning the Target Network in Function Space. CoRR abs/2406.01838 (2024) - 2023
- [j29]Eyal Gur, Alon Amar, Shoham Sabach:
Direct, fast and convergent solvers for the non-convex and non-smooth TDoA localization problem. Digit. Signal Process. 139: 104074 (2023) - [j28]Eyal Gur, Shoham Sabach, Shimrit Shtern:
Nested Alternating Minimization with FISTA for Non-convex and Non-smooth Optimization Problems. J. Optim. Theory Appl. 199(3): 1130-1157 (2023) - [j27]Eyal Gur, Shoham Sabach, Shimrit Shtern:
Convergent Nested Alternating Minimization Algorithms for Nonconvex Optimization Problems. Math. Oper. Res. 48(1): 53-77 (2023) - [j26]Roey Merchav, Shoham Sabach:
Convex Bi-level Optimization Problems with Nonsmooth Outer Objective Function. SIAM J. Optim. 33(4): 3114-3142 (2023) - [c4]Dan Garber, Tsur Livney, Shoham Sabach:
Faster Projection-Free Augmented Lagrangian Methods via Weak Proximal Oracle. AISTATS 2023: 7213-7238 - [c3]Kavosh Asadi, Rasool Fakoor, Shoham Sabach:
Resetting the Optimizer in Deep RL: An Empirical Study. NeurIPS 2023 - [c2]Kavosh Asadi, Shoham Sabach, Yao Liu, Omer Gottesman, Rasool Fakoor:
TD Convergence: An Optimization Perspective. NeurIPS 2023 - [i11]Kavosh Asadi, Shoham Sabach, Yao Liu, Omer Gottesman, Rasool Fakoor:
TD Convergence: An Optimization Perspective. CoRR abs/2306.17750 (2023) - [i10]Kavosh Asadi, Rasool Fakoor, Shoham Sabach:
Resetting the Optimizer in Deep RL: An Empirical Study. CoRR abs/2306.17833 (2023) - [i9]Roey Merchav, Shoham Sabach:
Convex Bi-Level Optimization Problems with Non-smooth Outer Objective Function. CoRR abs/2307.08245 (2023) - [i8]Zuxin Liu, Jesse Zhang, Kavosh Asadi, Yao Liu, Ding Zhao, Shoham Sabach, Rasool Fakoor:
TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models. CoRR abs/2310.05905 (2023) - 2022
- [j25]Shoham Sabach, Marc Teboulle:
Faster Lagrangian-Based Methods in Convex Optimization. SIAM J. Optim. 32(1): 204-227 (2022) - [c1]Egor Markovskiy, Fiana Raiber, Shoham Sabach, Oren Kurland:
From Cluster Ranking to Document Ranking. SIGIR 2022: 2137-2141 - [i7]Dan Garber, Tsur Livney, Shoham Sabach:
Faster Projection-Free Augmented Lagrangian Methods via Weak Proximal Oracle. CoRR abs/2210.13968 (2022) - 2021
- [j24]Dan Garber, Atara Kaplan, Shoham Sabach:
Improved complexities of conditional gradient-type methods with applications to robust matrix recovery problems. Math. Program. 186(1): 185-208 (2021) - 2020
- [j23]Aviv Gibali, Shoham Sabach, Sergey Voldman:
Non-Convex Split Feasibility Problems: Models, Algorithms and Theory. Open J. Math. Optim. 1: 1-15 (2020) - [j22]Tamir Hazan, Shoham Sabach, Sergey Voldman:
Stochastic proximal linear method for structured non-convex problems. Optim. Methods Softw. 35(5): 921-937 (2020) - [j21]Mahesh Chandra Mukkamala, Peter Ochs, Thomas Pock, Shoham Sabach:
Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Nonconvex Optimization. SIAM J. Math. Data Sci. 2(3): 658-682 (2020) - [j20]Eyal Gur, Shoham Sabach, Shimrit Shtern:
Alternating Minimization Based First-Order Method for the Wireless Sensor Network Localization Problem. IEEE Trans. Signal Process. 68: 6418-6431 (2020) - [i6]Eyal Gur, Shoham Sabach, Shimrit Shtern:
Alternating Minimization Based First-Order Method for the Wireless Sensor Network Localization Problem. CoRR abs/2010.06959 (2020) - [i5]Shoham Sabach, Marc Teboulle:
Faster Lagrangian-Based Methods in Convex Optimization. CoRR abs/2010.14314 (2020)
2010 – 2019
- 2019
- [j19]D. Russell Luke, Shoham Sabach, Marc Teboulle:
Optimization on Spheres: Models and Proximal Algorithms with Computational Performance Comparisons. SIAM J. Math. Data Sci. 1(3): 408-445 (2019) - [i4]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) - 2018
- [j18]Jérôme Bolte, Shoham Sabach, Marc Teboulle:
Nonconvex Lagrangian-Based Optimization: Monitoring Schemes and Global Convergence. Math. Oper. Res. 43(4): 1210-1232 (2018) - [j17]Amir Beck, Edouard Pauwels, Shoham Sabach:
Primal and dual predicted decrease approximation methods. Math. Program. 167(1): 37-73 (2018) - [j16]Jérôme Bolte, Shoham Sabach, Marc Teboulle, Yakov Vaisbourd:
First Order Methods Beyond Convexity and Lipschitz Gradient Continuity with Applications to Quadratic Inverse Problems. SIAM J. Optim. 28(3): 2131-2151 (2018) - [j15]Edouard Pauwels, Amir Beck, Yonina C. Eldar, Shoham Sabach:
On Fienup Methods for Sparse Phase Retrieval. IEEE Trans. Signal Process. 66(4): 982-991 (2018) - [i3]Dan Garber, Shoham Sabach, Atara Kaplan:
Fast Generalized Conditional Gradient Method with Applications to Matrix Recovery Problems. CoRR abs/1802.05581 (2018) - 2017
- [j14]D. Russell Luke, Shoham Sabach, Marc Teboulle, Kobi Zatlawey:
A simple globally convergent algorithm for the nonsmooth nonconvex single source localization problem. J. Glob. Optim. 69(4): 889-909 (2017) - [j13]Shoham Sabach, Shimrit Shtern:
A First Order Method for Solving Convex Bilevel Optimization Problems. SIAM J. Optim. 27(2): 640-660 (2017) - [i2]Edouard Pauwels, Amir Beck, Yonina C. Eldar, Shoham Sabach:
On Fienup Methods for Regularized Phase Retrieval. CoRR abs/1702.08339 (2017) - [i1]Jérôme Bolte, Shoham Sabach, Marc Teboulle, Yakov Vaisbourd:
First Order Methods beyond Convexity and Lipschitz Gradient Continuity with Applications to Quadratic Inverse Problems. CoRR abs/1706.06461 (2017) - 2016
- [j12]Thomas Pock, Shoham Sabach:
Inertial Proximal Alternating Linearized Minimization (iPALM) for Nonconvex and Nonsmooth Problems. SIAM J. Imaging Sci. 9(4): 1756-1787 (2016) - [j11]Amir Beck, Shoham Sabach, Marc Teboulle:
An Alternating Semiproximal Method for Nonconvex Regularized Structured Total Least Squares Problems. SIAM J. Matrix Anal. Appl. 37(3): 1129-1150 (2016) - 2015
- [j10]Amir Beck, Shoham Sabach:
Weiszfeld's Method: Old and New Results. J. Optim. Theory Appl. 164(1): 1-40 (2015) - [j9]Yoel Drori, Shoham Sabach, Marc Teboulle:
A simple algorithm for a class of nonsmooth convex-concave saddle-point problems. Oper. Res. Lett. 43(2): 209-214 (2015) - [j8]Robert Hesse, D. Russell Luke, Shoham Sabach, Matthew K. Tam:
Proximal Heterogeneous Block Implicit-Explicit Method and Application to Blind Ptychographic Diffraction Imaging. SIAM J. Imaging Sci. 8(1): 426-457 (2015) - [j7]Amir Beck, Edouard Pauwels, Shoham Sabach:
The Cyclic Block Conditional Gradient Method for Convex Optimization Problems. SIAM J. Optim. 25(4): 2024-2049 (2015) - 2014
- [j6]Jérôme Bolte, Shoham Sabach, Marc Teboulle:
Proximal alternating linearized minimization for nonconvex and nonsmooth problems. Math. Program. 146(1-2): 459-494 (2014) - [j5]Amir Beck, Shoham Sabach:
A first order method for finding minimal norm-like solutions of convex optimization problems. Math. Program. 147(1-2): 25-46 (2014) - 2012
- [j4]Regina Sandra Burachik, C. Yalçin Kaya, Shoham Sabach:
A Generalized Univariate Newton Method Motivated by Proximal Regularization. J. Optim. Theory Appl. 155(3): 923-940 (2012) - [j3]Amir Beck, Shoham Sabach:
An improved ellipsoid method for solving convex differentiable optimization problems. Oper. Res. Lett. 40(6): 541-545 (2012) - 2011
- [j2]Shoham Sabach:
Products of Finitely Many Resolvents of Maximal Monotone Mappings in Reflexive Banach Spaces. SIAM J. Optim. 21(4): 1289-1308 (2011) - [j1]Gábor Kassay, Simeon Reich, Shoham Sabach:
Iterative Methods for Solving Systems of Variational Inequalities in Reflexive Banach Spaces. SIAM J. Optim. 21(4): 1319-1344 (2011) - [p1]Simeon Reich, Shoham Sabach:
Existence and Approximation of Fixed Points of Bregman Firmly Nonexpansive Mappings in Reflexive Banach Spaces. Fixed-Point Algorithms for Inverse Problems in Science and Engineering 2011: 301-316
Coauthor Index
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