11 papers
Sample Complexity Analysis for Constrained Bilevel Reinforcement Learning
Naman Saxena, Vaneet Aggarwal
Several important problem settings within the literature of reinforcement learning (RL), such as meta-learning, hierarchical learning, and RL from human feedback (RL-HF), can be mo…
Order-Optimal Sample Complexity of Rectified Flows
Hari Krishna Sahoo, Mudit Gaur, Vaneet Aggarwal
Recently, flow-based generative models have shown superior efficiency compared to diffusion models. In this paper, we study rectified flow models, which constrain transport traject…
Generative Modeling with Continuous Flows: Sample Complexity of Flow Matching
Mudit Gaur, Prashant Trivedi, Shuchin Aeron +3
Flow matching has recently emerged as a promising alternative to diffusion-based generative models, offering faster sampling and simpler training by learning continuous flows gover…
ECPv2: Fast, Efficient, and Scalable Global Optimization of Lipschitz Functions
Fares Fourati, Mohamed-Slim Alouini, Vaneet Aggarwal
We propose ECPv2, a scalable and theoretically grounded algorithm for global optimization of Lipschitz-continuous functions with unknown Lipschitz constants. Building on the Every…
Primal-Only Actor Critic Algorithm for Robust Constrained Average Cost MDPs
Anirudh Satheesh, Sooraj Sathish, Swetha Ganesh +2
In this work, we study the problem of finding robust and safe policies in Robust Constrained Average-Cost Markov Decision Processes (RCMDPs). A key challenge in this setting is the…
Regret Analysis of Average-Reward Unichain MDPs via an Actor-Critic Approach
Swetha Ganesh, Vaneet Aggarwal
Actor-Critic methods are widely used for their scalability, yet existing theoretical guarantees for infinite-horizon average-reward Markov Decision Processes (MDPs) often rely on r…