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06/25/2023
Is RLHF More Difficult than Standard RL?
Reinforcement learning from Human Feedback (RLHF) learns from preference...
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02/13/2023
Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation
A unique challenge in Multi-Agent Reinforcement Learning (MARL) is the c...
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10/20/2022
Learning Rationalizable Equilibria in Multiplayer Games
A natural goal in multiagent learning besides finding equilibria is to l...
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03/14/2022
Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits
An ideal strategy in zero-sum games should not only grant the player an ...
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02/28/2022
Neural Adaptive SCEne Tracing
Neural rendering with implicit neural networks has recently emerged as a...
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02/04/2022
NeAT: Neural Adaptive Tomography
In this paper, we present Neural Adaptive Tomography (NeAT), the first a...
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10/27/2021
V-Learning – A Simple, Efficient, Decentralized Algorithm for Multiagent RL
A major challenge of multiagent reinforcement learning (MARL) is the cur...
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03/23/2021
An Exponential Lower Bound for Linearly-Realizable MDPs with Constant Suboptimality Gap
A fundamental question in the theory of reinforcement learning is: suppo...
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02/18/2021
Don't Fix What ain't Broke: Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization
Minimax optimization has recently gained a lot of attention as adversari...
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10/28/2020
Provably Efficient Online Agnostic Learning in Markov Games
We study online agnostic learning, a problem that arises in episodic mul...
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08/21/2020
Refined Analysis of FPL for Adversarial Markov Decision Processes
We consider the adversarial Markov Decision Process (MDP) problem, where...
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08/17/2020
On the Suboptimality of Negative Momentum for Minimax Optimization
Smooth game optimization has recently attracted great interest in machin...
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06/11/2020
Improved Algorithms for Convex-Concave Minimax Optimization
This paper studies minimax optimization problems min_x max_y f(x,y), whe...
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10/16/2019
On Solving Minimax Optimization Locally: A Follow-the-Ridge Approach
Many tasks in modern machine learning can be formulated as finding equil...
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04/12/2019
Distributed Bandit Learning: How Much Communication is Needed to Achieve (Near) Optimal Regret
We study the communication complexity of distributed multi-armed bandits...
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01/27/2019