Anas Barakat
Research Fellow
Singapore University of Technology and Design
My research develops foundations and algorithms for sequential and strategic decision-making when objectives extend beyond expected cumulative reward, feedback is limited, and multiple adaptive agents interact. My work lies at the intersection of optimization, reinforcement learning, and learning in games. I am particularly motivated by emerging challenges in modern AI, including policy optimization for LLM post-training, preference-based learning, and decentralized learning in competitive and cooperative multi-agent systems.
I am currently a Research Fellow at Singapore University of Technology and Design, working with Georgios Piliouras and Antonios Varvitsiotis. Previously, I was a postdoctoral fellow at ETH Zurich working with Niao He. I received my PhD in mathematics and computer science from Institut Polytechnique de Paris (Télécom Paris), advised by Pascal Bianchi and Walid Hachem.
Optimization for Sequential and Strategic Decision-Making
My research develops foundations and algorithms for decision-making beyond classical assumptions on objectives, feedback, and agent interactions, spanning three complementary directions:
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Policy optimization beyond expected additive rewards: Foundations and optimization algorithms for reinforcement learning with objectives beyond standard expected additive rewards, including general utilities and LLM post-training objectives.
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Learning and optimization in structured games: Foundations and algorithms for learning in strategic and dynamic multi-agent environments, with a focus on the long-run behavior of learning dynamics and convergence to equilibria.
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Online learning beyond convexity under limited feedback: Understanding when learning remains possible with nonconvex objectives and only partial or ordinal feedback. I develop algorithms and guarantees by exploiting latent structure—such as hidden convexity—and learning directly from bandit or preference feedback.
Selected & Recent Research
For a complete list of my publications, see below or my Google Scholar.
News
Experience and Background
Here is my CV for more information.
Research
Publications
Policy Optimization Beyond Expected Additive Rewards
Keywords: policy gradient methods, general utility RL, convex RL, LLM post-training.
Learning in Structured Dynamical Games
Keywords: learning in games, game dynamics, multi-agent RL, Markov games with general utilities, Markov potential games, continuous games with state dynamics, game theory.
Online Learning under Limited Feedback
Keywords: online learning, bandit feedback, preference feedback, hidden convexity, non-convex optimization.
Stochastic Optimization
Keywords: Stochastic approximation, Dynamical systems, non-convex stochastic optimization, adaptive gradient methods.
Academic Service
- Area Chair: ICLR 2027.
- Conference Reviewing: NeurIPS (‘22, ‘23, ‘25, ‘26), ICML (‘25, Gold Reviewer ‘26), ICLR (‘25, ‘26), AISTATS (Top Reviewer ‘22, ‘24, ‘25, ‘26), EC (‘26).
- Journal Reviewing: 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), IEEE Transactions on Automatic Control.
Teaching
SUTD (2024-2025):
- Co-instructor and course designer ( Online Learning and Learning in Games) with John Lazarsfeld and Iosif Sakos.
ETH Zurich (2022-2024):
- Teaching Assistant and instructor for 2 lectures for the course Optimization for Data Science (Prof. Niao He).
- Head Teaching Assistant and instructor for 1 lecture (on value-based methods) for Foundations of Reinforcement Learning (Prof. Niao He).
- Coordinator of the seminar course ‘Advanced Topics in Machine Learning’.
Télécom Paris (2018-2021): Teaching Assistant
Optimization for Machine Learning (graduate level), Statistics (graduate level), Probabilities (undergraduate level).