
A Provably Efficient Algorithm for Linear Markov Decision Process with Low Switching Cost
Many realworld applications, such as those in medical domains, recommen...
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Minimax Sample Complexity for Turnbased Stochastic Game
The empirical success of Multiagent reinforcement learning is encouragi...
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Accommodating Picky Customers: Regret Bound and Exploration Complexity for MultiObjective Reinforcement Learning
In this paper we consider multiobjective reinforcement learning where t...
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Episodic Linear Quadratic Regulators with Lowrank Transitions
Linear Quadratic Regulators (LQR) achieve enormous successful realworld...
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Random Walk Bandits
Bandit learning problems find important applications ranging from medica...
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Is Plugin Solver SampleEfficient for Featurebased Reinforcement Learning?
It is believed that a modelbased approach for reinforcement learning (R...
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Toward the Fundamental Limits of Imitation Learning
Imitation learning (IL) aims to mimic the behavior of an expert policy i...
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Obtaining Adjustable Regularization for Free via Iterate Averaging
Regularization for optimization is a crucial technique to avoid overfitt...
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ModelBased MultiAgent RL in ZeroSum Markov Games with NearOptimal Sample Complexity
Modelbased reinforcement learning (RL), which finds an optimal policy u...
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On RewardFree Reinforcement Learning with Linear Function Approximation
Rewardfree reinforcement learning (RL) is a framework which is suitable...
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Qlearning with Logarithmic Regret
This paper presents the first nonasymptotic result showing that a model...
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Preferencebased Reinforcement Learning with FiniteTime Guarantees
Preferencebased Reinforcement Learning (PbRL) replaces reward values in...
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ModelBased Reinforcement Learning with ValueTargeted Regression
This paper studies modelbased reinforcement learning (RL) for regret mi...
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Provably Efficient Reinforcement Learning with General Value Function Approximation
Value function approximation has demonstrated phenomenal empirical succe...
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Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning?
Learning to plan for long horizons is a central challenge in episodic re...
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Provably Efficient Exploration for RL with Unsupervised Learning
We study how to use unsupervised learning for efficient exploration in r...
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Sketching Transformed Matrices with Applications to Natural Language Processing
Suppose we are given a large matrix A=(a_i,j) that cannot be stored in m...
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Does Knowledge Transfer Always Help to Learn a Better Policy?
One of the key approaches to save samples when learning a policy for a r...
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Continuous Control with Contexts, Provably
A fundamental challenge in artificial intelligence is to build an agent ...
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Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?
Modern deep learning methods provide an effective means to learn good re...
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Efficient Symmetric Norm Regression via Linear Sketching
We provide efficient algorithms for overconstrained linear regression pr...
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Solving Discounted Stochastic TwoPlayer Games with NearOptimal Time and Sample Complexity
In this paper, we settle the sampling complexity of solving discounted t...
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On the Optimality of Sparse ModelBased Planning for Markov Decision Processes
This work considers the sample complexity of obtaining an ϵoptimal poli...
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FeatureBased QLearning for TwoPlayer Stochastic Games
Consider a twoplayer zerosum stochastic game where the transition func...
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Reinforcement Leaning in Feature Space: Matrix Bandit, Kernels, and Regret Bound
Exploration in reinforcement learning (RL) suffers from the curse of dim...
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Learning to Control in Metric Space with Optimal Regret
We study online reinforcement learning for finitehorizon deterministic ...
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The OneWay Communication Complexity of Dynamic Time Warping Distance
We resolve the randomized oneway communication complexity of Dynamic Ti...
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SampleOptimal Parametric QLearning with Linear Transition Models
Consider a Markov decision process (MDP) that admits a set of stateacti...
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Towards a Theoretical Understanding of HashingBased Neural Nets
Parameter reduction has been an important topic in deep learning due to ...
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Universal Streaming of Subset Norms
Most known algorithms in the streaming model of computation aim to appro...
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On Landscape of Lagrangian Functions and Stochastic Search for Constrained Nonconvex Optimization
We study constrained nonconvex optimization problems in machine learning...
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Revisiting Frequency Moment Estimation in Random Order Streams
We revisit one of the classic problems in the data stream literature, na...
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Variance Reduction Methods for Sublinear Reinforcement Learning
This work considers the problem of provably optimal reinforcement learni...
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Sensitivity Sampling Over Dynamic Geometric Data Streams with Applications to kClustering
Sensitivity based sampling is crucial for constructing nearlyoptimal co...
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Misspecified Nonconvex Statistical Optimization for Phase Retrieval
Existing nonconvex statistical optimization theory and methods crucially...
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Approximate Convex Hull of Data Streams
Given a finite set of points P ⊆R^d, we would like to find a small subse...
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On Quadratic Convergence of DC Proximal Newton Algorithm for Nonconvex Sparse Learning in High Dimensions
We propose a DC proximal Newton algorithm for solving nonconvex regulari...
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Online Factorization and Partition of Complex Networks From Random Walks
Finding the reduceddimensional structure is critical to understanding c...
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Dropping Convexity for More Efficient and Scalable Online Multiview Learning
Multiview representation learning is very popular for latent factor anal...
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Lin F. Yang
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