b'Zhihua Zhang'

research

∙ 08/15/2023

Near-Optimal Last-iterate Convergence of Policy Optimization in Zero-sum Polymatrix Markov games

Computing approximate Nash equilibria in multi-player general-sum Markov...

0 Zailin Ma, et al. ∙

research

∙ 07/10/2023

Enhancing Adversarial Robustness via Score-Based Optimization

Adversarial attacks have the potential to mislead deep neural network cl...

0 Boya Zhang, et al. ∙

research

∙ 07/04/2023

Training Energy-Based Models with Diffusion Contrastive Divergences

Energy-Based Models (EBMs) have been widely used for generative modeling...

0 Weijian Luo, et al. ∙

research

∙ 06/08/2023

Entropy-based Training Methods for Scalable Neural Implicit Sampler

Efficiently sampling from un-normalized target distributions is a fundam...

0 Weijian Luo, et al. ∙

research

∙ 05/29/2023

Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models

Due to the ease of training, ability to scale, and high sample quality, ...

0 Weijian Luo, et al. ∙

research

∙ 04/29/2023

Semi-Infinitely Constrained Markov Decision Processes and Efficient Reinforcement Learning

We propose a novel generalization of constrained Markov decision process...

11 Liangyu Zhang, et al. ∙

research

∙ 04/15/2023

Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis

We study finite-sum distributed optimization problems with n-clients und...

0 Dachao Lin, et al. ∙

research

∙ 02/02/2023

Avoiding Model Estimation in Robust Markov Decision Processes with a Generative Model

Robust Markov Decision Processes (MDPs) are getting more attention for l...

0 Wenhao Yang, et al. ∙

research

∙ 09/12/2022

Statistical Estimation of Confounded Linear MDPs: An Instrumental Variable Approach

In an Markov decision process (MDP), unobservable confounders may exist ...

0 Miao Lu, et al. ∙

research

∙ 05/19/2022

Sparse Adversarial Attack in Multi-agent Reinforcement Learning

Cooperative multi-agent reinforcement learning (cMARL) has many real app...

0 Yizheng Hu, et al. ∙

research

∙ 05/17/2022

On the Convergence of Policy in Unregularized Policy Mirror Descent

In this short note, we give the convergence analysis of the policy in th...

0 Dachao Lin, et al. ∙

research

∙ 04/06/2022

Federated Reinforcement Learning with Environment Heterogeneity

We study a Federated Reinforcement Learning (FedRL) problem in which n a...

1 Hao Jin, et al. ∙

research

∙ 01/08/2022

Global Convergence Analysis of Deep Linear Networks with A One-neuron Layer

In this paper, we follow Eftekhari's work to give a non-local convergenc...

0 Kun Chen, et al. ∙

research

∙ 10/16/2021

Greedy and Random Broyden's Methods with Explicit Superlinear Convergence Rates in Nonlinear Equations

In this paper, we propose the greedy and random Broyden's method for sol...

0 Haishan Ye, et al. ∙

research

∙ 05/31/2021

Memory-Efficient Differentiable Transformer Architecture Search

Differentiable architecture search (DARTS) is successfully applied in ma...

0 Yuekai Zhao, et al. ∙

research

∙ 05/09/2021

Directional Convergence Analysis under Spherically Symmetric Distribution

We consider the fundamental problem of learning linear predictors (i.e.,...

0 Dachao Lin, et al. ∙

research

∙ 05/09/2021

Non-asymptotic Performances of Robust Markov Decision Processes

In this paper, we study the non-asymptotic performance of optimal policy...

4 Wenhao Yang, et al. ∙

research

∙ 04/12/2021

Meta-Regularization: An Approach to Adaptive Choice of the Learning Rate in Gradient Descent

We propose Meta-Regularization, a novel approach for the adaptive choice...

0 Guangzeng Xie, et al. ∙

research

∙ 03/15/2021

Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Construction

The contribution of this paper includes two aspects. First, we study the...

0 Yuze Han, et al. ∙

research

∙ 03/15/2021

DIPPA: An improved Method for Bilinear Saddle Point Problems

This paper studies bilinear saddle point problems min_xmax_y g(x) + x^⊤A...

0 Guangzeng Xie, et al. ∙

research

∙ 09/16/2020

Landscape of Sparse Linear Network: A Brief Investigation

Network pruning, or sparse network has a long history and practical sign...

1 Dachao Lin, et al. ∙

research

∙ 08/30/2020

Optimal Quantization for Batch Normalization in Neural Network Deployments and Beyond

Quantized Neural Networks (QNNs) use low bit-width fixed-point numbers f...

0 Dachao Lin, et al. ∙

research

∙ 08/09/2020

Intervention Generative Adversarial Networks

In this paper we propose a novel approach for stabilizing the training p...

24 Jiadong Liang, et al. ∙

research

∙ 07/11/2020

An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter Optimization

The evaluation of hyperparameters, neural architectures, or data augment...

0 Yimin Huang, et al. ∙

research

∙ 10/21/2019

Communication Efficient Decentralized Training with Multiple Local Updates

Communication efficiency plays a significant role in decentralized optim...

0 Xiang Li, et al. ∙

research

∙ 10/02/2019

Distillation ≈ Early Stopping? Harvesting Dark Knowledge Utilizing Anisotropic Information Retrieval For Overparameterized Neural Network

Distillation is a method to transfer knowledge from one model to another...

0 Bin Dong, et al. ∙

research

∙ 09/13/2019

A Stochastic Proximal Point Algorithm for Saddle-Point Problems

We consider saddle point problems which objective functions are the aver...

0 Luo Luo, et al. ∙

research

∙ 08/22/2019

A General Analysis Framework of Lower Complexity Bounds for Finite-Sum Optimization

This paper studies the lower bound complexity for the optimization probl...

0 Guangzeng Xie, et al. ∙

research

∙ 08/18/2019

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks

Stochastic variance-reduced gradient (SVRG) is a classical optimization ...

0 Hao Jin, et al. ∙

research

∙ 07/04/2019

On the Convergence of FedAvg on Non-IID Data

Federated learning enables a large amount of edge computing devices to l...

0 Xiang Li, et al. ∙

research

∙ 05/28/2019

A Gram-Gauss-Newton Method Learning Overparameterized Deep Neural Networks for Regression Problems

First-order methods such as stochastic gradient descent (SGD) are curren...

0 Tianle Cai, et al. ∙

research

∙ 03/02/2019

A Unified Framework for Regularized Reinforcement Learning

We propose and study a general framework for regularized Markov decision...

0 Xiang Li, et al. ∙

research

∙ 02/15/2019

Lipschitz Generative Adversarial Nets

In this paper we study the convergence of generative adversarial network...

0 Zhiming Zhou, et al. ∙

research

∙ 02/13/2019

Do Subsampled Newton Methods Work for High-Dimensional Data?

Subsampled Newton methods approximate Hessian matrices through subsampli...

0 Xiang Li, et al. ∙

research

∙ 08/10/2018

Hierarchical Attention: What Really Counts in Various NLP Tasks

Attention mechanisms in sequence to sequence models have shown great abi...

0 Zehao Dou, et al. ∙

research

∙ 05/17/2018

Interpolatron: Interpolation or Extrapolation Schemes to Accelerate Optimization for Deep Neural Networks

In this paper we explore acceleration techniques for large scale nonconv...

0 Guangzeng Xie, et al. ∙

research

∙ 02/27/2017

A Unifying Framework for Convergence Analysis of Approximate Newton Methods

Many machine learning models are reformulated as optimization problems. ...

0 Haishan Ye, et al. ∙

research

∙ 08/16/2016

An Efficient Character-Level Neural Machine Translation

Neural machine translation aims at building a single large neural networ...

0 Shenjian Zhao, et al. ∙

research

∙ 01/31/2016

A Proximal Stochastic Quasi-Newton Algorithm

In this paper, we discuss the problem of minimizing the sum of two conve...

0 Luo Luo, et al. ∙

research

∙ 11/18/2015

Wishart Mechanism for Differentially Private Principal Components Analysis

We propose a new input perturbation mechanism for publishing a covarianc...

0 Wuxuan Jiang, et al. ∙

research

∙ 10/29/2015

Nonconvex Penalization in Sparse Estimation: An Approach Based on the Bernstein Function

In this paper we study nonconvex penalization using Bernstein functions ...

0 Zhihua Zhang, et al. ∙

research

∙ 10/26/2015

A Parallel algorithm for X-Armed bandits

The target of X-armed bandit problem is to find the global maximum of an...

0 Cheng Chen, et al. ∙

research

∙ 09/08/2015

A Scalable and Extensible Framework for Superposition-Structured Models

In many learning tasks, structural models usually lead to better interpr...

0 Shenjian Zhao, et al. ∙

research

∙ 12/26/2014

Adjusting Leverage Scores by Row Weighting: A Practical Approach to Coherent Matrix Completion

Low-rank matrix completion is an important problem with extensive real-w...

0 Shusen Wang, et al. ∙

research

∙ 10/03/2014

Group Orbit Optimization: A Unified Approach to Data Normalization

In this paper we propose and study an optimization problem over a matrix...

0 Shuchang Zhou, et al. ∙

research

∙ 12/17/2013

The Bernstein Function: A Unifying Framework of Nonconvex Penalization in Sparse Estimation

In this paper we study nonconvex penalization using Bernstein functions....

0 Zhihua Zhang, et al. ∙

research

∙ 12/17/2013

The Matrix Ridge Approximation: Algorithms and Applications

We are concerned with an approximation problem for a symmetric positive ...

0 Zhihua Zhang, et al. ∙

research

∙ 08/28/2013

Compound Poisson Processes, Latent Shrinkage Priors and Bayesian Nonconvex Penalization

In this paper we discuss Bayesian nonconvex penalization for sparse lear...

0 Zhihua Zhang, et al. ∙

research

∙ 07/22/2013

Kinetic Energy Plus Penalty Functions for Sparse Estimation

In this paper we propose and study a family of sparsity-inducing penalty...

0 Zhihua Zhang, et al. ∙

research

∙ 10/04/2012

A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound

The CUR matrix decomposition is an important extension of Nyström approx...

0 Shusen Wang, et al. ∙

Zhihua Zhang

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