39 citations · 56 across the 13 of their papers we have counts for
15 papers
Weak Convergence Rates for Partial-Sum Processes of Nonlinear Stochastic Approximation
Xiang Li, Jiadong Liang, Zhihua Zhang
Stochastic approximation provides a general framework for online estimation and optimization. Statistical inference based on the resulting estimates requires understanding their fl…
Decoupled Functional Central Limit Theorems for Two-Time-Scale Stochastic Approximation
Yuze Han, Xiang Li, Jiadong Liang +1
In two-time-scale stochastic approximation (SA), two iterates are updated at different rates, governed by distinct step sizes, with each update influencing the other. Previous stud…
Asymptotic Time-Uniform Inference for Parameters in Averaged Stochastic Approximation
Chuhan Xie, Kaicheng Jin, Jiadong Liang +1
We study time-uniform statistical inference for parameters in stochastic approximation (SA), which encompasses a bunch of applications in optimization and machine learning. To that…
Gradient Tracking for High Dimensional Federated Optimization
Jiadong Liang, Yang Peng, Zhihua Zhang
In this paper, we study the (decentralized) distributed optimization problem with high-dimensional sparse structure. Building upon the FedDA algorithm, we propose a (Decentralized)…
Estimation and Inference in Distributional Reinforcement Learning
Liangyu Zhang, Yang Peng, Jiadong Liang +2
In this paper, we study distributional reinforcement learning from the perspective of statistical efficiency. We investigate distributional policy evaluation, aiming to estimate th…
Convergence and Inference of Stream SGD, with Applications to Queueing Systems and Inventory Control
Xiang Li, Jiadong Liang, Xinyun Chen +1
Stream stochastic gradient descent (SGD) is a simple and efficient method for solving online optimization problems in operations research (OR), where data is generated by parameter…
Asymptotic Behaviors and Phase Transitions in Projected Stochastic Approximation: A Jump Diffusion Approach
Jiadong Liang, Yuze Han, Xiang Li +1
In this paper we consider linearly constrained optimization problems and propose a loopless projection stochastic approximation (LPSA) algorithm. It performs the projection with pr…
Online Statistical Inference for Nonlinear Stochastic Approximation with Markovian Data
Xiang Li, Jiadong Liang, Zhihua Zhang
Many stochastic approximation (SA) algorithms evolve along a single trajectory, making uncertainty quantification challenging under nonlinear dynamics and Markov dependence. We dev…
Layout-Bridging Text-to-Image Synthesis
Jiadong Liang, Wenjie Pei, Feng Lu
The crux of text-to-image synthesis stems from the difficulty of preserving the cross-modality semantic consistency between the input text and the synthesized image. Typical method…
A Statistical Analysis of Polyak-Ruppert Averaged Q-learning
Xiang Li, Wenhao Yang, Jiadong Liang +2
We study Q-learning with Polyak-Ruppert averaging in a discounted Markov decision process in synchronous and tabular settings. Under a Lipschitz condition, we establish a functiona…
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…
CPGAN: Full-Spectrum Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis
Jiadong Liang, Wenjie Pei, Feng Lu
Typical methods for text-to-image synthesis seek to design effective generative architecture to model the text-to-image mapping directly. It is fairly arduous due to the cross-moda…
Lipschitz Generative Adversarial Nets
Zhiming Zhou, Jiadong Liang, Yuxuan Song +5
In this paper, we study the convergence of generative adversarial networks (GANs) from the perspective of the informativeness of the gradient of the optimal discriminative function…
Understanding the Effectiveness of Lipschitz-Continuity in Generative Adversarial Nets
Zhiming Zhou, Yuxuan Song, Lantao Yu +5
In this paper, we investigate the underlying factor that leads to failure and success in the training of GANs. We study the property of the optimal discriminative function and show…