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Books and Theses
- 2021
[b1]Anas Barakat:
Contributions to non-convex stochastic optimization and reinforcement learning. (Contributions à l'optimisation stochastique non convexe et à l'apprentissage par renforcement). Institut Polytechnique de Paris, France, 2021
Journal Articles
- 2021
- [j1]Anas Barakat, Pascal Bianchi:
Convergence and Dynamical Behavior of the ADAM Algorithm for Nonconvex Stochastic Optimization. SIAM J. Optim. 31(1): 244-274 (2021)
Conference and Workshop Papers
- 2024
- [c6]Philip Jordan, Anas Barakat, Niao He:
Independent Learning in Constrained Markov Potential Games. AISTATS 2024: 4024-4032 - 2023
- [c5]Jiduan Wu, Anas Barakat, Ilyas Fatkhullin, Niao He:
Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity. CDC 2023: 2602-2609 - [c4]Anas Barakat, Ilyas Fatkhullin, Niao He:
Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space. ICML 2023: 1753-1800 - [c3]Ilyas Fatkhullin, Anas Barakat, Anastasia Kireeva, Niao He:
Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies. ICML 2023: 9827-9869 - 2022
- [c2]Anas Barakat, Pascal Bianchi, Julien Lehmann:
Analysis of a Target-Based Actor-Critic Algorithm with Linear Function Approximation. AISTATS 2022: 991-1040 - 2020
- [c1]Anas Barakat, Pascal Bianchi:
Convergence Rates of a Momentum Algorithm with Bounded Adaptive Step Size for Nonconvex Optimization. ACML 2020: 225-240
Informal and Other Publications
- 2024
- [i9]Philip Jordan, Anas Barakat, Niao He:
Independent Learning in Constrained Markov Potential Games. CoRR abs/2402.17885 (2024) - [i8]Kimon Protopapas, Anas Barakat:
Policy Mirror Descent with Lookahead. CoRR abs/2403.14156 (2024) - [i7]Pragnya Alatur, Anas Barakat, Niao He:
Independent Policy Mirror Descent for Markov Potential Games: Scaling to Large Number of Players. CoRR abs/2408.08075 (2024) - 2023
- [i6]Ilyas Fatkhullin, Anas Barakat, Anastasia Kireeva, Niao He:
Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies. CoRR abs/2302.01734 (2023) - [i5]Anas Barakat, Ilyas Fatkhullin, Niao He:
Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space. CoRR abs/2306.01854 (2023) - [i4]Jiduan Wu, Anas Barakat, Ilyas Fatkhullin
, Niao He:
Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity. CoRR abs/2309.04272 (2023) - 2021
- [i3]Anas Barakat, Pascal Bianchi, Julien Lehmann:
Analysis of a Target-Based Actor-Critic Algorithm with Linear Function Approximation. CoRR abs/2106.07472 (2021) - 2019
- [i2]Anas Barakat, Pascal Bianchi:
Convergence Analysis of a Momentum Algorithm with Adaptive Step Size for Non Convex Optimization. CoRR abs/1911.07596 (2019) - 2018
- [i1]Anas Barakat, Pascal Bianchi:
Convergence of the ADAM algorithm from a Dynamical System Viewpoint. CoRR abs/1810.02263 (2018)
Coauthor Index
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