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09/15/2023
Deep Reinforcement Learning for Efficient and Fair Allocation of Health Care Resources
Scarcity of health care resources could result in the unavoidable conseq...
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08/03/2023
Aligning Agent Policy with Externalities: Reward Design via Bilevel RL
In reinforcement learning (RL), a reward function is often assumed at th...
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07/26/2023
Actions Speak What You Want: Provably Sample-Efficient Reinforcement Learning of the Quantal Stackelberg Equilibrium from Strategic Feedbacks
We study reinforcement learning (RL) for learning a Quantal Stackelberg ...
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07/24/2023
Provable Benefits of Policy Learning from Human Preferences in Contextual Bandit Problems
A crucial task in decision-making problems is reward engineering. It is ...
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07/13/2023
Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement
We explore the methodology and theory of reward-directed generation via ...
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07/06/2023
Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight
This paper studies the sample-efficiency of learning in Partially Observ...
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07/05/2023
Scaling In-Context Demonstrations with Structured Attention
The recent surge of large language models (LLMs) highlights their abilit...
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07/04/2023
Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks
Convolutional residual neural networks (ConvResNets), though overparamet...
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06/26/2023
Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories
Existing theories on deep nonparametric regression have shown that when ...
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06/22/2023
Visual Adversarial Examples Jailbreak Large Language Models
Recently, there has been a surge of interest in introducing vision into ...
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06/21/2023
Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP
In this paper, we study representation learning in partially observable ...
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06/13/2023
Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective
Off-policy Learning to Rank (LTR) aims to optimize a ranker from data co...
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06/02/2023
Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations
In real-world reinforcement learning (RL) systems, various forms of impa...
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05/30/2023
Adversarial Attacks on Online Learning to Rank with Stochastic Click Models
We propose the first study of adversarial attacks on online learning to ...
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05/29/2023
Reinforcement Learning with Human Feedback: Learning Dynamic Choices via Pessimism
In this paper, we study offline Reinforcement Learning with Human Feedba...
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05/23/2023
Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challenges
Latest developments in computer hardware, sensor technologies, and artif...
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05/23/2023
ChipGPT: How far are we from natural language hardware design
As large language models (LLMs) like ChatGPT exhibited unprecedented mac...
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02/20/2023
Deep Reinforcement Learning for Cost-Effective Medical Diagnosis
Dynamic diagnosis is desirable when medical tests are costly or time-con...
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02/14/2023
Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data
Diffusion models achieve state-of-the-art performance in various generat...
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01/28/2023
STEERING: Stein Information Directed Exploration for Model-Based Reinforcement Learning
Directed Exploration is a crucial challenge in reinforcement learning (R...
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12/01/2022
Near Sample-Optimal Reduction-based Policy Learning for Average Reward MDP
This work considers the sample complexity of obtaining an ε-optimal poli...
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11/02/2022
Energy System Digitization in the Era of AI: A Three-Layered Approach towards Carbon Neutrality
The transition towards carbon-neutral electricity is one of the biggest ...
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10/30/2022
Representation Learning for General-sum Low-rank Markov Games
We study multi-agent general-sum Markov games with nonlinear function ap...
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10/03/2022
Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient
Offline reinforcement learning, which aims at optimizing sequential deci...
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06/29/2022
Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization
Online influence maximization aims to maximize the influence spread of a...
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06/22/2022
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks
Bilevel optimization have gained growing interests, with numerous applic...
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06/10/2022
Offline Stochastic Shortest Path: Learning, Evaluation and Towards Optimality
Goal-oriented Reinforcement Learning, where the agent needs to reach the...
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06/10/2022
Communication Efficient Distributed Learning for Kernelized Contextual Bandits
We tackle the communication efficiency challenge of learning kernelized ...
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06/06/2022
Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks
We consider the off-policy evaluation problem of reinforcement learning ...
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06/05/2022
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization
Directed Evolution (DE), a landmark wet-lab method originated in 1960s, ...
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06/01/2022
Byzantine-Robust Online and Offline Distributed Reinforcement Learning
We consider a distributed reinforcement learning setting where multiple ...
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05/29/2022
Provable Benefits of Representational Transfer in Reinforcement Learning
We study the problem of representational transfer in RL, where an agent ...
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05/23/2022
Parameter-Efficient Sparsity for Large Language Models Fine-Tuning
With the dramatically increased number of parameters in language models,...
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03/11/2022
Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism
Offline reinforcement learning, which seeks to utilize offline/historica...
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02/10/2022
Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory
Off-Policy Evaluation (OPE) serves as one of the cornerstones in Reinfor...
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01/31/2022
Optimal Estimation of Off-Policy Policy Gradient via Double Fitted Iteration
Policy gradient (PG) estimation becomes a challenge when we are not allo...
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01/31/2022
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach
We present BRIEE (Block-structured Representation learning with Interlea...
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09/24/2021
Optimal policy evaluation using kernel-based temporal difference methods
We study methods based on reproducing kernel Hilbert spaces for estimati...
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07/16/2021
Boosting the Convergence of Reinforcement Learning-based Auto-pruning Using Historical Data
Recently, neural network compression schemes like channel pruning have b...
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06/15/2021
On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control
Reinforcement learning is a framework for interactive decision-making wi...
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06/04/2021
You Only Compress Once: Towards Effective and Elastic BERT Compression via Exploit-Explore Stochastic Nature Gradient
Despite superior performance on various natural language processing task...
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05/31/2021
1×N Block Pattern for Network Sparsity
Though network sparsity emerges as a promising direction to overcome the...
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05/29/2021
MARL with General Utilities via Decentralized Shadow Reward Actor-Critic
We posit a new mechanism for cooperation in multi-agent reinforcement le...
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05/24/2021
Towards Compact CNNs via Collaborative Compression
Channel pruning and tensor decomposition have received extensive attenti...
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05/17/2021
Thin-Film Smoothed Particle Hydrodynamics Fluid
We propose a particle-based method to simulate thin-film fluid that join...
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05/03/2021
Learning Good State and Action Representations via Tensor Decomposition
The transition kernel of a continuous-state-action Markov decision proce...
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02/17/2021
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method
Policy gradient gives rise to a rich class of reinforcement learning (RL...
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02/06/2021
Bootstrapping Statistical Inference for Off-Policy Evaluation
Bootstrapping provides a flexible and effective approach for assessing t...
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11/09/2020
On Function Approximation in Reinforcement Learning: Optimism in the Face of Large State Spaces
The classical theory of reinforcement learning (RL) has focused on tabul...
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11/08/2020
