
Pure Exploration in Kernel and Neural Bandits
We study pure exploration in bandits, where the dimension of the feature...
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VarianceAware OffPolicy Evaluation with Linear Function Approximation
We study the offpolicy evaluation (OPE) problem in reinforcement learni...
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Provably Efficient Representation Learning in Lowrank Markov Decision Processes
The success of deep reinforcement learning (DRL) is due to the power of ...
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UniformPAC Bounds for Reinforcement Learning with Linear Function Approximation
We study reinforcement learning (RL) with linear function approximation....
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Batched Neural Bandits
In many sequential decisionmaking problems, the individuals are split i...
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Nearly Optimal Regret for Learning Adversarial MDPs with Linear Function Approximation
We study the reinforcement learning for finitehorizon episodic Markov d...
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Almost Optimal Algorithms for Twoplayer Markov Games with Linear Function Approximation
We study reinforcement learning for twoplayer zerosum Markov games wit...
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Nearly Minimax Optimal Regret for Learning Infinitehorizon Averagereward MDPs with Linear Function Approximation
We study reinforcement learning in an infinitehorizon averagereward se...
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Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints
We study reinforcement learning (RL) with linear function approximation ...
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Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes
We study reinforcement learning (RL) with linear function approximation ...
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Logarithmic Regret for Reinforcement Learning with Linear Function Approximation
Reinforcement learning (RL) with linear function approximation has recei...
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Provable MultiObjective Reinforcement Learning with Generative Models
Multiobjective reinforcement learning (MORL) is an extension of ordinar...
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Neural Thompson Sampling
Thompson Sampling (TS) is one of the most effective algorithms for solvi...
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Minimax Optimal Reinforcement Learning for Discounted MDPs
We study the reinforcement learning problem for discounted Markov Decisi...
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Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping
Modern tasks in reinforcement learning are always with large state and a...
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Neural Contextual Bandits with Upper Confidence BoundBased Exploration
We study the stochastic contextual bandit problem, where the reward is g...
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Stochastic Recursive VarianceReduced Cubic Regularization Methods
Stochastic VarianceReduced Cubic regularization (SVRC) algorithms have ...
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Lower Bounds for Smooth Nonconvex FiniteSum Optimization
Smooth finitesum optimization has been widely studied in both convex an...
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Sample Efficient Stochastic VarianceReduced Cubic Regularization Method
We propose a sample efficient stochastic variancereduced cubic regulari...
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Stochastic Gradient Descent Optimizes Overparameterized Deep ReLU Networks
We study the problem of training deep neural networks with Rectified Lin...
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On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization
Adaptive gradient methods are workhorses in deep learning. However, the ...
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Finding Local Minima via Stochastic Nested Variance Reduction
We propose two algorithms that can find local minima faster than the sta...
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Stochastic Nested Variance Reduction for Nonconvex Optimization
We study finitesum nonconvex optimization problems, where the objective...
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Stochastic VarianceReduced Cubic Regularized Newton Method
We propose a stochastic variancereduced cubic regularized Newton method...
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Dongruo Zhou
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