979 citations · 1.5k across the 63 of their papers we have counts for
13 papers · 1 filter
Statistical Estimation of Confounded Linear MDPs: An Instrumental Variable Approach
Miao Lu, Wenhao Yang, Liangyu Zhang +1
In an Markov decision process (MDP), unobservable confounders may exist and have impacts on the data generating process, so that the classic off-policy evaluation (OPE) estimators…
Statistical Estimation and Inference via Local SGD in Federated Learning
Xiang Li, Jiadong Liang, Xiangyu Chang +1
Federated Learning (FL) makes a large amount of edge computing devices (e.g., mobile phones) jointly learn a global model without data sharing. In FL, data are generated in a decen…
Intervention Generative Adversarial Networks
Jiadong Liang, Liangyu Zhang, Cheng Zhang +1
In this paper we propose a novel approach for stabilizing the training process of Generative Adversarial Networks as well as alleviating the mode collapse problem. The main idea is…
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter Optimization
Yimin Huang, Yujun Li, Hanrong Ye +2
The evaluation of hyperparameters, neural architectures, or data augmentation policies becomes a critical model selection problem in advanced deep learning with a large hyperparame…
Communication-Efficient Distributed SVD via Local Power Iterations
Xiang Li, Shusen Wang, Kun Chen +1
We study distributed computing of the truncated singular value decomposition problem. We develop an algorithm that we call \texttt{LocalPower} for improving communication efficienc…
Distillation Early Stopping? Harvesting Dark Knowledge Utilizing Anisotropic Information Retrieval For Overparameterized Neural Network
Bin Dong, Jikai Hou, Yiping Lu +1
Distillation is a method to transfer knowledge from one model to another and often achieves higher accuracy with the same capacity. In this paper, we aim to provide a theoretical u…
Communication-Efficient Local Decentralized SGD Methods
Xiang Li, Wenhao Yang, Shusen Wang +1
Recently, the technique of local updates is a powerful tool in centralized settings to improve communication efficiency via periodical communication. For decentralized settings, it…
Towards Better Generalization: BP-SVRG in Training Deep Neural Networks
Hao Jin, Dachao Lin, Zhihua Zhang
Stochastic variance-reduced gradient (SVRG) is a classical optimization method. Although it is theoretically proved to have better convergence performance than stochastic gradient…
On the Convergence of FedAvg on Non-IID Data
Xiang Li, Kaixuan Huang, Wenhao Yang +2
Federated learning enables a large amount of edge computing devices to jointly learn a model without data sharing. As a leading algorithm in this setting, Federated Averaging (\tex…
A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning
Xiang Li, Wenhao Yang, Zhihua Zhang
We propose and study a general framework for regularized Markov decision processes (MDPs) where the goal is to find an optimal policy that maximizes the expected discounted total r…
Do Subsampled Newton Methods Work for High-Dimensional Data?
Xiang Li, Shusen Wang, Zhihua Zhang
Subsampled Newton methods approximate Hessian matrices through subsampling techniques, alleviating the cost of forming Hessian matrices but using sufficient curvature information.…
Interpolatron: Interpolation or Extrapolation Schemes to Accelerate Optimization for Deep Neural Networks
Guangzeng Xie, Yitan Wang, Shuchang Zhou +1
In this paper we explore acceleration techniques for large scale nonconvex optimization problems with special focuses on deep neural networks. The extrapolation scheme is a classic…
A Parallel algorithm for -Armed bandits
Cheng Chen, Shuang Liu, Zhihua Zhang +1
The target of -armed bandit problem is to find the global maximum of an unknown stochastic function , given a finite budget of evaluations. Recently, $\mathcal{…