research
∙
09/13/2023
PIAVE: A Pose-Invariant Audio-Visual Speaker Extraction Network
It is common in everyday spoken communication that we look at the turnin...
share
research
∙
06/25/2023
Is RLHF More Difficult than Standard RL?
Reinforcement learning from Human Feedback (RLHF) learns from preference...
research
∙
06/22/2023
Context-lumpable stochastic bandits
We consider a contextual bandit problem with S contexts and A actions. I...
research
∙
05/18/2023
Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL
While policy optimization algorithms have played an important role in re...
research
∙
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...
research
∙
09/29/2022
Optimistic MLE – A Generic Model-based Algorithm for Partially Observable Sequential Decision Making
This paper introduces a simple efficient learning algorithms for general...
research
∙
07/25/2022
Dive into Big Model Training
The increasing scale of model size and continuous improvement of perform...
research
∙
07/18/2022
A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games
This paper proposes novel, end-to-end deep reinforcement learning algori...
research
∙
06/06/2022
Policy Optimization for Markov Games: Unified Framework and Faster Convergence
This paper studies policy optimization algorithms for multi-agent reinfo...
research
∙
06/02/2022
Sample-Efficient Reinforcement Learning of Partially Observable Markov Games
This paper considers the challenging tasks of Multi-Agent Reinforcement ...
research
∙
05/27/2022
Pseudo-Mallows for Efficient Probabilistic Preference Learning
We propose the Pseudo-Mallows distribution over the set of all permutati...
research
∙
04/19/2022
When Is Partially Observable Reinforcement Learning Not Scary?
Applications of Reinforcement Learning (RL), in which agents learn to ma...
research
∙
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 ...
research
∙
11/07/2021
LiMuSE: Lightweight Multi-modal Speaker Extraction
The past several years have witnessed significant progress in modeling t...
research
∙
10/27/2021
V-Learning – A Simple, Efficient, Decentralized Algorithm for Multiagent RL
A major challenge of multiagent reinforcement learning (MARL) is the cur...
research
∙
06/07/2021
The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces
Modern reinforcement learning (RL) commonly engages practical problems w...
research
∙
02/01/2021
Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms
Finding the minimal structural assumptions that empower sample-efficient...
research
∙
12/24/2020
A Tight Lower Bound for Uniformly Stable Algorithms
Leveraging algorithmic stability to derive sharp generalization bounds i...
research
∙
10/04/2020
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play
Model-based algorithms—algorithms that decouple learning of the model an...
research
∙
07/15/2020
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
In federated optimization, heterogeneity in the clients' local datasets ...
research
∙
06/22/2020
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs
Partial observability is a common challenge in many reinforcement learni...
research
∙
04/05/2019
Diverse personalized recommendations with uncertainty from implicit preference data with the Bayesian Mallows Model
Clicking data, which exists in abundance and contains objective user pre...
research
∙
02/22/2019
BayesMallows: An R Package for the Bayesian Mallows Model
BayesMallows is an R package for analyzing data in the form of rankings ...
research
∙
01/30/2018
