
Deep Reinforcement Learning with Smooth Policy
Deep neural networks have been widely adopted in modern reinforcement le...
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Convergent Policy Optimization for Safe Reinforcement Learning
We study the safe reinforcement learning problem with nonlinear function...
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On Computation and Generalization of Generative Adversarial Imitation Learning
Generative Adversarial Imitation Learning (GAIL) is a powerful and pract...
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Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees
Graph representation learning is a ubiquitous task in machine learning w...
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Natural ActorCritic Converges Globally for Hierarchical Linear Quadratic Regulator
Multiagent reinforcement learning has been successfully applied to a nu...
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More Supervision, Less Computation: StatisticalComputational Tradeoffs in Weakly Supervised Learning
We consider the weakly supervised binary classification problem where th...
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Provably Efficient Reinforcement Learning with Linear Function Approximation
Modern Reinforcement Learning (RL) is commonly applied to practical prob...
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On Tighter Generalization Bound for Deep Neural Networks: CNNs, ResNets, and Beyond
Our paper proposes a generalization error bound for a general family of ...
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Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes
Solving statistical learning problems often involves nonconvex optimizat...
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A convex formulation for highdimensional sparse sliced inverse regression
Sliced inverse regression is a popular tool for sufficient dimension red...
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Symmetry, Saddle Points, and Global Geometry of Nonconvex Matrix Factorization
We propose a general theory for studying the geometry of nonconvex objec...
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Sparse Tensor Graphical Model: Nonconvex Optimization and Statistical Inference
We consider the estimation and inference of sparse graphical models that...
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NESTT: A Nonconvex PrimalDual Splitting Method for Distributed and Stochastic Optimization
We study a stochastic and distributed algorithm for nonconvex problems w...
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Sparse Generalized Eigenvalue Problem: Optimal Statistical Rates via Truncated Rayleigh Flow
Sparse generalized eigenvalue problem plays a pivotal role in a large fa...
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Sharp ComputationalStatistical Phase Transitions via Oracle Computational Model
We study the fundamental tradeoffs between computational tractability an...
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Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference
We study parameter estimation and asymptotic inference for sparse nonlin...
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Optimal linear estimation under unknown nonlinear transform
Linear regression studies the problem of estimating a model parameter β^...
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Statistical Limits of Convex Relaxations
Many high dimensional sparse learning problems are formulated as nonconv...
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High Dimensional ExpectationMaximization Algorithm: Statistical Optimization and Asymptotic Normality
We provide a general theory of the expectationmaximization (EM) algorit...
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Sparse Principal Component Analysis for High Dimensional Vector Autoregressive Models
We study sparse principal component analysis for high dimensional vector...
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Optimal computational and statistical rates of convergence for sparse nonconvex learning problems
We provide theoretical analysis of the statistical and computational pro...
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Misspecified Nonconvex Statistical Optimization for Phase Retrieval
Existing nonconvex statistical optimization theory and methods crucially...
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Recovery of simultaneous low rank and twoway sparse coefficient matrices, a nonconvex approach
We study the problem of recovery of matrices that are simultaneously low...
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Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models
We propose a nonparametric method for detecting nonlinear causal relatio...
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MultiAgent Reinforcement Learning via Double Averaging PrimalDual Optimization
Despite the success of singleagent reinforcement learning, multiagent ...
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OffPolicy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy
When learning from a batch of logged bandit feedback, the discrepancy be...
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Highdimensional Varying Index Coefficient Models via Stein's Identity
We study the parameter estimation problem for a singleindex varying coe...
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Provable Gaussian Embedding with One Observation
The success of machine learning methods heavily relies on having an appr...
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Curse of Heterogeneity: Computational Barriers in Sparse Mixture Models and Phase Retrieval
We study the fundamental tradeoffs between statistical accuracy and comp...
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A Theoretical Analysis of Deep QLearning
Despite the great empirical success of deep reinforcement learning, its ...
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On the Global Convergence of Imitation Learning: A Case for Linear Quadratic Regulator
We study the global convergence of generative adversarial imitation lear...
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A MultiAgent OffPolicy ActorCritic Algorithm for Distributed Reinforcement Learning
This paper extends offpolicy reinforcement learning to the multiagent ...
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Neural TemporalDifference Learning Converges to Global Optima
Temporaldifference learning (TD), coupled with neural networks, is amon...
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Neural Proximal/Trust Region Policy Optimization Attains Globally Optimal Policy
Proximal policy optimization and trust region policy optimization (PPO a...
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On the Global Convergence of ActorCritic: A Case for Linear Quadratic Regulator with Ergodic Cost
Despite the empirical success of the actorcritic algorithm, its theoret...
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Fast multiagent temporaldifference learning via homotopy stochastic primaldual optimization
We consider a distributed multiagent policy evaluation problem in reinf...
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Neural Policy Gradient Methods: Global Optimality and Rates of Convergence
Policy gradient methods with actorcritic schemes demonstrate tremendous...
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A CommunicationEfficient MultiAgent ActorCritic Algorithm for Distributed Reinforcement Learning
This paper considers a distributed reinforcement learning problem in whi...
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Provably Efficient Exploration in Policy Optimization
While policybased reinforcement learning (RL) achieves tremendous succe...
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Credible Sample Elicitation by Deep Learning, for Deep Learning
It is important to collect credible training samples (x,y) for building ...
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Pontryagin Differentiable Programming: An EndtoEnd Learning and Control Framework
This paper develops a Pontryagin differentiable programming (PDP) method...
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ActorCritic Provably Finds Nash Equilibria of LinearQuadratic MeanField Games
We study discretetime meanfield Markov games with infinite numbers of ...
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Upper Confidence PrimalDual Optimization: Stochastically Constrained Markov Decision Processes with Adversarial Losses and Unknown Transitions
We consider online learning for episodic Markov decision processes (MDPs...
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Provably Efficient Safe Exploration via PrimalDual Policy Optimization
We study the Safe Reinforcement Learning (SRL) problem using the Constra...
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Learning ZeroSum SimultaneousMove Markov Games Using Function Approximation and Correlated Equilibrium
We develop provably efficient reinforcement learning algorithms for two...
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Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Rate
Generative adversarial imitation learning (GAIL) demonstrates tremendous...
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Zhaoran Wang
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Graduate student in the Department of Operations Research and Financial Engineering at Princeton University