Zhaoran Wang

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Princeton University ∙ Georgia Institute of Technology ∙ Johns Hopkins University

Graduate student in the Department of Operations Research and Financial Engineering at Princeton University

Deep Reinforcement Learning with Smooth Policy

Deep neural networks have been widely adopted in modern reinforcement le...
03/21/2020 ∙ by Qianli Shen, et al. ∙ 54 ∙ share

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Convergent Policy Optimization for Safe Reinforcement Learning

We study the safe reinforcement learning problem with nonlinear function...
10/26/2019 ∙ by Ming Yu, et al. ∙ 29 ∙ share

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On Computation and Generalization of Generative Adversarial Imitation Learning

Generative Adversarial Imitation Learning (GAIL) is a powerful and pract...
01/09/2020 ∙ by Minshuo Chen, et al. ∙ 15 ∙ share

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Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees

Graph representation learning is a ubiquitous task in machine learning w...
03/02/2020 ∙ by Sen Na, et al. ∙ 13 ∙ share

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Natural Actor-Critic Converges Globally for Hierarchical Linear Quadratic Regulator

Multi-agent reinforcement learning has been successfully applied to a nu...
12/14/2019 ∙ by Yuwei Luo, et al. ∙ 7 ∙ share

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More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning

We consider the weakly supervised binary classification problem where th...
07/14/2019 ∙ by Xinyang Yi, et al. ∙ 6 ∙ share

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Provably Efficient Reinforcement Learning with Linear Function Approximation

Modern Reinforcement Learning (RL) is commonly applied to practical prob...
07/11/2019 ∙ by Chi Jin, et al. ∙ 3 ∙ share

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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 ...
06/13/2018 ∙ by Xingguo Li, et al. ∙ 2 ∙ share

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Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes

Solving statistical learning problems often involves nonconvex optimizat...
08/29/2018 ∙ by Chris Junchi Li, et al. ∙ 2 ∙ share

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A convex formulation for high-dimensional sparse sliced inverse regression

Sliced inverse regression is a popular tool for sufficient dimension red...
09/17/2018 ∙ by Kean Ming Tan, et al. ∙ 2 ∙ share

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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...
12/29/2016 ∙ by Xingguo Li, et al. ∙ 0 ∙ share

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Sparse Tensor Graphical Model: Non-convex Optimization and Statistical Inference

We consider the estimation and inference of sparse graphical models that...
09/15/2016 ∙ by Will Wei Sun, et al. ∙ 0 ∙ share

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NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization

We study a stochastic and distributed algorithm for nonconvex problems w...
05/25/2016 ∙ by Davood Hajinezhad, et al. ∙ 0 ∙ share

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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...
04/29/2016 ∙ by Kean Ming Tan, et al. ∙ 0 ∙ share

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Sharp Computational-Statistical Phase Transitions via Oracle Computational Model

We study the fundamental tradeoffs between computational tractability an...
12/30/2015 ∙ by Zhaoran Wang, et al. ∙ 0 ∙ share

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Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference

We study parameter estimation and asymptotic inference for sparse nonlin...
11/14/2015 ∙ by Zhuoran Yang, et al. ∙ 0 ∙ share

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Optimal linear estimation under unknown nonlinear transform

Linear regression studies the problem of estimating a model parameter β^...
05/13/2015 ∙ by Xinyang Yi, et al. ∙ 0 ∙ share

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Statistical Limits of Convex Relaxations

Many high dimensional sparse learning problems are formulated as nonconv...
03/04/2015 ∙ by Zhaoran Wang, et al. ∙ 0 ∙ share

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High Dimensional Expectation-Maximization Algorithm: Statistical Optimization and Asymptotic Normality

We provide a general theory of the expectation-maximization (EM) algorit...
12/30/2014 ∙ by Zhaoran Wang, et al. ∙ 0 ∙ share

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Sparse Principal Component Analysis for High Dimensional Vector Autoregressive Models

We study sparse principal component analysis for high dimensional vector...
06/30/2013 ∙ by Zhaoran Wang, et al. ∙ 0 ∙ share

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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...
06/20/2013 ∙ by Zhaoran Wang, et al. ∙ 0 ∙ share

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Misspecified Nonconvex Statistical Optimization for Phase Retrieval

Existing nonconvex statistical optimization theory and methods crucially...
12/18/2017 ∙ by Zhuoran Yang, et al. ∙ 0 ∙ share

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Recovery of simultaneous low rank and two-way sparse coefficient matrices, a nonconvex approach

We study the problem of recovery of matrices that are simultaneously low...
02/20/2018 ∙ by Ming Yu, et al. ∙ 0 ∙ share

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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...
03/11/2018 ∙ by Yingxiang Yang, et al. ∙ 0 ∙ share

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Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization

Despite the success of single-agent reinforcement learning, multi-agent ...
06/03/2018 ∙ by Hoi-To Wai, et al. ∙ 0 ∙ share

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Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy

When learning from a batch of logged bandit feedback, the discrepancy be...
08/01/2018 ∙ by Yuan Xie, et al. ∙ 0 ∙ share

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High-dimensional Varying Index Coefficient Models via Stein's Identity

We study the parameter estimation problem for a single-index varying coe...
10/16/2018 ∙ by Sen Na, et al. ∙ 0 ∙ share

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Provable Gaussian Embedding with One Observation

The success of machine learning methods heavily relies on having an appr...
10/25/2018 ∙ by Ming Yu, et al. ∙ 0 ∙ share

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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...
08/21/2018 ∙ by Jianqing Fan, et al. ∙ 0 ∙ share

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A Theoretical Analysis of Deep Q-Learning

Despite the great empirical success of deep reinforcement learning, its ...
01/01/2019 ∙ by Zhuora Yang, et al. ∙ 0 ∙ share

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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...
01/11/2019 ∙ by Qi Cai, et al. ∙ 0 ∙ share

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A Multi-Agent Off-Policy Actor-Critic Algorithm for Distributed Reinforcement Learning

This paper extends off-policy reinforcement learning to the multi-agent ...
03/15/2019 ∙ by Wesley Suttle, et al. ∙ 0 ∙ share

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Neural Temporal-Difference Learning Converges to Global Optima

Temporal-difference learning (TD), coupled with neural networks, is amon...
05/24/2019 ∙ by Qi Cai, et al. ∙ 0 ∙ share

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Neural Proximal/Trust Region Policy Optimization Attains Globally Optimal Policy

Proximal policy optimization and trust region policy optimization (PPO a...
06/25/2019 ∙ by Boyi Liu, et al. ∙ 0 ∙ share

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On the Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost

Despite the empirical success of the actor-critic algorithm, its theoret...
07/14/2019 ∙ by Zhuoran Yang, et al. ∙ 0 ∙ share

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Fast multi-agent temporal-difference learning via homotopy stochastic primal-dual optimization

We consider a distributed multi-agent policy evaluation problem in reinf...
08/07/2019 ∙ by Dongsheng Ding, et al. ∙ 0 ∙ share

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Neural Policy Gradient Methods: Global Optimality and Rates of Convergence

Policy gradient methods with actor-critic schemes demonstrate tremendous...
08/29/2019 ∙ by Lingxiao Wang, et al. ∙ 0 ∙ share

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A Communication-Efficient Multi-Agent Actor-Critic Algorithm for Distributed Reinforcement Learning

This paper considers a distributed reinforcement learning problem in whi...
07/06/2019 ∙ by Yixuan Lin, et al. ∙ 0 ∙ share

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Provably Efficient Exploration in Policy Optimization

While policy-based reinforcement learning (RL) achieves tremendous succe...
12/12/2019 ∙ by Qi Cai, et al. ∙ 0 ∙ share

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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 ...
10/08/2019 ∙ by Yang Liu, et al. ∙ 0 ∙ share

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Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework

This paper develops a Pontryagin differentiable programming (PDP) method...
12/30/2019 ∙ by Wanxin Jin, et al. ∙ 0 ∙ share

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Actor-Critic Provably Finds Nash Equilibria of Linear-Quadratic Mean-Field Games

We study discrete-time mean-field Markov games with infinite numbers of ...
10/16/2019 ∙ by Zuyue Fu, et al. ∙ 0 ∙ share

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Upper Confidence Primal-Dual Optimization: Stochastically Constrained Markov Decision Processes with Adversarial Losses and Unknown Transitions

We consider online learning for episodic Markov decision processes (MDPs...
03/02/2020 ∙ by Shuang Qiu, et al. ∙ 0 ∙ share

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Provably Efficient Safe Exploration via Primal-Dual Policy Optimization

We study the Safe Reinforcement Learning (SRL) problem using the Constra...
03/01/2020 ∙ by Dongsheng Ding, et al. ∙ 0 ∙ share

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Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibrium

We develop provably efficient reinforcement learning algorithms for two-...
02/17/2020 ∙ by Qiaomin Xie, et al. ∙ 0 ∙ share

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Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Rate

Generative adversarial imitation learning (GAIL) demonstrates tremendous...
03/08/2020 ∙ by Yufeng Zhang, et al. ∙ 0 ∙ share

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Zhaoran Wang

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