
Linear Convergence of EntropyRegularized Natural Policy Gradient with Linear Function Approximation
Natural policy gradient (NPG) methods with function approximation achiev...
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The Complexity of NonconvexStronglyConcave Minimax Optimization
This paper studies the complexity for finding approximate stationary poi...
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Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction
The building sector consumes the largest energy in the world, and there ...
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Sample Complexity and Overparameterization Bounds for ProjectionFree Neural TD Learning
We study the dynamics of temporaldifference learning with neural networ...
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ProvablyEfficient Double QLearning
In this paper, we establish a theoretical comparison between the asympto...
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Biased Stochastic Gradient Descent for Conditional Stochastic Optimization
Conditional Stochastic Optimization (CSO) covers a variety of applicatio...
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Periodic QLearning
The use of target networks is a common practice in deep reinforcement le...
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Global Convergence and VarianceReduced Optimization for a Class of NonconvexNonconcave Minimax Problems
Nonconvex minimax problems appear frequently in emerging machine learnin...
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A Unified Switching System Perspective and O.D.E. Analysis of QLearning Algorithms
In this paper, we introduce a unified framework for analyzing a large fa...
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Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents
This article reviews recent advances in multiagent reinforcement learni...
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Sample Complexity of Sample Average Approximation for Conditional Stochastic Optimization
In this paper, we study a class of stochastic optimization problems, ref...
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Exponential Family Estimation via Adversarial Dynamics Embedding
We present an efficient algorithm for maximum likelihood estimation (MLE...
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TargetBased Temporal Difference Learning
The use of target networks has been a popular and key component of recen...
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Quadratic Decomposable Submodular Function Minimization: Theory and Practice
We introduce a new convex optimization problem, termed quadratic decompo...
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Kernel Exponential Family Estimation via Doubly Dual Embedding
We investigate penalized maximum loglikelihood estimation for exponenti...
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Quadratic Decomposable Submodular Function Minimization
We introduce a new convex optimization problem, termed quadratic decompo...
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Nonparametric Hawkes Processes: Online Estimation and Generalization Bounds
In this paper, we design a nonparametric online algorithm for estimating...
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Smoothed Dual Embedding Control
We revisit the Bellman optimality equation with Nesterov's smoothing tec...
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Boosting the Actor with Dual Critic
This paper proposes a new actorcriticstyle algorithm called Dual Actor...
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Stochastic Generative Hashing
Learningbased binary hashing has become a powerful paradigm for fast se...
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Fast and Simple Optimization for Poisson Likelihood Models
Poisson likelihood models have been prevalently used in imaging, social ...
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Learning from Conditional Distributions via Dual Embeddings
Many machine learning tasks, such as learning with invariance and policy...
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Provable Bayesian Inference via Particle Mirror Descent
Bayesian methods are appealing in their flexibility in modeling complex ...
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Scalable Kernel Methods via Doubly Stochastic Gradients
The general perception is that kernel methods are not scalable, and neur...
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Stochastic ADMM for Nonsmooth Optimization
We present a stochastic setting for optimization problems with nonsmooth...
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Niao He
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