
Online SubSampling for Reinforcement Learning with General Function Approximation
Designing provably efficient algorithms with general function approximat...
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An Exponential Lower Bound for LinearlyRealizable MDPs with Constant Suboptimality Gap
A fundamental question in the theory of reinforcement learning is: suppo...
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Bilinear Classes: A Structural Framework for Provable Generalization in RL
This work introduces Bilinear Classes, a new structural framework, which...
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Instabilities of Offline RL with PreTrained Neural Representation
In offline reinforcement learning (RL), we seek to utilize offline data ...
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What are the Statistical Limits of Offline RL with Linear Function Approximation?
Offline reinforcement learning seeks to utilize offline (observational) ...
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Planning with Submodular Objective Functions
We study planning with submodular objective functions, where instead of ...
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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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Preferencebased Reinforcement Learning with FiniteTime Guarantees
Preferencebased Reinforcement Learning (PbRL) replaces reward values in...
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Nearly Linear Row Sampling Algorithm for Quantile Regression
We give a row sampling algorithm for the quantile loss function with sam...
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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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Agnostic Qlearning with Function Approximation in Deterministic Systems: Tight Bounds on Approximation Error and Sample Complexity
The current paper studies the problem of agnostic Qlearning with functi...
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Optimism in Reinforcement Learning with Generalized Linear Function Approximation
We design a new provably efficient algorithm for episodic reinforcement ...
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Enhanced Convolutional Neural Tangent Kernels
Recent research shows that for training with ℓ_2 loss, convolutional neu...
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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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Harnessing the Power of Infinitely Wide Deep Nets on Smalldata Tasks
Recent research shows that the following two models are equivalent: (a) ...
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Provably Efficient Qlearning with Function Approximation via Distribution Shift Error Checking Oracle
Qlearning with function approximation is one of the most popular method...
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The Communication Complexity of Optimization
We consider the communication complexity of a number of distributed opti...
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Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
While graph kernels (GKs) are easy to train and enjoy provable theoretic...
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Dimensionality Reduction for Tukey Regression
We give the first dimensionality reduction methods for the overconstrain...
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On Exact Computation with an Infinitely Wide Neural Net
How well does a classic deep net architecture like AlexNet or VGG19 clas...
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Tight Bounds for the Subspace Sketch Problem with Applications
In the subspace sketch problem one is given an n× d matrix A with O((nd)...
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FineGrained Analysis of Optimization and Generalization for Overparameterized TwoLayer Neural Networks
Recent works have cast some light on the mystery of why deep nets fit an...
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Classical Algorithms from Quantum and ArthurMerlin Communication Protocols
The polynomial method from circuit complexity has been applied to severa...
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Tight Bounds for ℓ_p Oblivious Subspace Embeddings
An ℓ_p oblivious subspace embedding is a distribution over r × n matrice...
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Nearly Optimal Sampling Algorithms for Combinatorial Pure Exploration
We study the combinatorial pure exploration problem BestSet in stochast...
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Ruosong Wang
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