b'Zhihua Zhang'

research

∙ 05/19/2022

Sparse Adversarial Attack in Multi-agent Reinforcement Learning

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

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...

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...

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...

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...

Haishan Ye, et al. ∙

research

∙ 05/31/2021

Memory-Efficient Differentiable Transformer Architecture Search

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

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.,...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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 ...

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...

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...

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...

Xiang Li, et al. ∙

research

∙ 02/15/2019

Lipschitz Generative Adversarial Nets

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

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...

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...

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...

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. ...

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...

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...

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...

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 ...

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...

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...

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...

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...

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....

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 ...

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...

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...

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...

Shusen Wang, et al. ∙

research

∙ 06/27/2012

Bayesian Multicategory Support Vector Machines

We show that the multi-class support vector machine (MSVM) proposed by L...

Zhihua Zhang, et al. ∙

research

∙ 04/19/2012

EP-GIG Priors and Applications in Bayesian Sparse Learning

In this paper we propose a novel framework for the construction of spars...

Zhihua Zhang, et al. ∙

research

∙ 04/10/2012

Coherence Functions with Applications in Large-Margin Classification Methods

Support vector machines (SVMs) naturally embody sparseness due to their ...

Zhihua Zhang, et al. ∙

Zhihua Zhang

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