Anas Barakat

Anas Barakat

Research Fellow

Singapore University of Technology and Design

I am currently a research fellow at Singapore University of Technology and Design working with Georgios Piliouras and Antonios Varvitsiotis.

My research lies at the intersection of multi-agent learning, reinforcement learning, and optimization. I focus on the design and analysis of learning algorithms for sequential decision-making in strategic, dynamic, and uncertain environments. Motivated by challenges in machine learning and multi-agent systems, my work combines tools from game theory, reinforcement learning, stochastic optimization, and dynamical systems to understand and shape how autonomous agents learn to act, adapt, and collaborate in strategic and dynamic multi-agent environments.

Previously, I was a postdoctoral fellow at ETH Zurich working with Niao He in the department of computer science. I obtained my PhD in applied mathematics and computer science from Institut Polytechnique de Paris at Télécom Paris under the supervision of Pascal Bianchi and Walid Hachem. I received my engineering Master’s degree in applied mathematics and computer science from Télécom Paris and a Master’s degree in data science from Université Paris Saclay.

Here is my CV for more information.

Interests

  • Multi-Agent Learning
  • Reinforcement Learning
  • Optimization

Education

  • PhD in Applied Mathematics and CS, 2021

    Institut Polytechnique de Paris (Télécom Paris)

  • MSc in Data Science, 2018

    Université Paris Saclay

  • MSc in Applied Mathematics and CS, 2018

    Télécom Paris

Multi-Agent Learning

Anas Barakat, Wayne Lin, John Lazarsfeld, Antonios Varvitsiotis (2025). Optimistic Online Learning in Symmetric Cone Games. Under review.

Arxiv

Pragnya Alatur, Anas Barakat*, Niao He (2024). Independent Policy Mirror Descent for Markov Potential Games: Scaling to Large Number of Players. IEEE CDC 2024. *corresponding author.

Slides Proceedings Arxiv

Philip Jordan, Anas Barakat, Niao He (2023). Independent Learning in Constrained Markov Potential Games. AISTATS 2024.

Poster Slides Proceedings Arxiv

Jiduan Wu, Anas Barakat, Ilyas Fatkhullin, Niao He (2023). Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity. IEEE CDC 2023.

Proceedings Arxiv

Reinforcement Learning

Olivier Lepel, Anas Barakat (2024). A Prospect-Theoretic Policy Gradient Algorithm for Behavioral Alignment in Reinforcement Learning. Under review.

Slides Arxiv

Anas Barakat, Souradip Chakraborty, Peihong Yu, Pratap Tokekar, Amrit Singh Bedi (2024). Towards Scalable General Utility Reinforcement Learning: Occupancy Approximation, Sample Complexity and Global Optimality. Under review.

Arxiv

Kimon Protopapas, Anas Barakat (2024). Policy Mirror Descent with Lookahead. NeurIPS 2024.

Proceedings Arxiv

Anas Barakat, Ilyas Fatkhullin, Niao He (2023). Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space. ICML 2023.

Poster Slides Proceedings Arxiv

Ilyas Fatkhullin, Anas Barakat, Anastasia Kireeva, Niao He (2023). Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies. ICML 2023.

Poster Proceedings Arxiv

Anas Barakat, Pascal Bianchi, Julien Lehmann (2022). Analysis of a Target-Based Actor-Critic Algorithm with Linear Function Approximation. AISTATS 2022.

Poster Proceedings Arxiv

Optimization for Machine Learning

Anas Barakat, Pascal Bianchi, Walid Hachem, Sholom Schechtman (2021). Stochastic optimization with momentum: convergence, fluctuations, and traps avoidance. Electronic Journal of Statistics 15 (2), 3892-3947.

DOI Arxiv Journal

Anas Barakat, Pascal Bianchi (2020). Convergence and Dynamical Behavior of the ADAM Algorithm for Non-Convex Stochastic Optimization. SIAM Journal on Optimization, 31 (1), 244-274.

Poster Slides Video DOI Arxiv Journal

Anas Barakat, Pascal Bianchi (2019). Convergence Rates of a Momentum Algorithm with Bounded Adaptive Step Size for Non-Convex Optimization. ACML 2020.

Poster Slides Proceedings Arxiv

Anas Barakat, Pascal Bianchi (2019). Convergence de l'algorithme Adam du point de vue des systèmes dynamiques. GRETSI 2019.

PDF Slides

Talks

Invited talk - Finance and RL Talks
Apr 21, 2025 10:00 PM Online
Invited talk - 4th Symposium on Machine Learning and Dynamical Systems
Jul 8, 2024 2:45 PM Fields Institute, Toronto, Canada
Talk - SUTD Group Meeting
May 3, 2024 10:00 AM Online
Invited talk - ICCOPT 2022
Jul 28, 2022 3:00 PM Lehigh University, Bethlehem, USA
Invited talk - 14th CMStatistics International Conference
Dec 20, 2021 2:45 PM King’s College, London, United Kingdom (hybrid)
See all talks

Reviewing

  • Conferences: NeurIPS, ICML, ICLR, COLT, AISTATS, IEEE CDC.

  • Journals: Journal of Machine Learning Research (JMLR), Transactions on Machine Learning Research (TMLR), Mathematical Programming, SIAM Journal on Optimization (SIOPT), Journal of Optimization Theory and Applications (JOTA), Systems & Control Letters, Mathematics of Control, Signals, and Systems (MCSS), Stochastic Systems, IEEE Transactions on Automatic Control, IEEE Control Systems Letters.

Teaching

ETH Zurich (2022-2024):

Télécom Paris (2018-2021): Teaching Assistant

Contact