450 citations · 533 across the 14 of their papers we have counts for
14 papers
Noisy recovery from random linear observations: Sharp minimax rates under elliptical constraints
Reese Pathak, Martin J. Wainwright, Lin Xiao
Estimation problems with constrained parameter spaces arise in various settings. In many of these problems, the observations available to the statistician can be modelled as arisin…
Pairwise Instance Relation Augmentation for Long-tailed Multi-label Text Classification
Lin Xiao, Pengyu Xu, Liping Jing +1
Multi-label text classification (MLTC) is one of the key tasks in natural language processing. It aims to assign multiple target labels to one document. Due to the uneven popularit…
Faster Last-iterate Convergence of Policy Optimization in Zero-Sum Markov Games
Shicong Cen, Yuejie Chi, Simon S. Du +1
Multi-Agent Reinforcement Learning (MARL) -- where multiple agents learn to interact in a shared dynamic environment -- permeates across a wide range of critical applications. Whil…
Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies
Rui Yuan, Simon S. Du, Robert M. Gower +2
We consider infinite-horizon discounted Markov decision processes and study the convergence rates of the natural policy gradient (NPG) and the Q-NPG methods with the log-linear pol…
Grad-GradaGrad? A Non-Monotone Adaptive Stochastic Gradient Method
Aaron Defazio, Baoyu Zhou, Lin Xiao
The classical AdaGrad method adapts the learning rate by dividing by the square root of a sum of squared gradients. Because this sum on the denominator is increasing, the method ca…
On the Convergence Rates of Policy Gradient Methods
Lin Xiao
We consider infinite-horizon discounted Markov decision problems with finite state and action spaces and study the convergence rates of the projected policy gradient method and a g…
Does Head Label Help for Long-Tailed Multi-Label Text Classification
Lin Xiao, Xiangliang Zhang, Liping Jing +2
Multi-label text classification (MLTC) aims to annotate documents with the most relevant labels from a number of candidate labels. In real applications, the distribution of label f…
Stochastic optimization with decision-dependent distributions
Dmitriy Drusvyatskiy, Lin Xiao
Stochastic optimization problems often involve data distributions that change in reaction to the decision variables. This is the case for example when members of the population res…
Jointly Modeling Intra- and Inter-transaction Dependencies with Hierarchical Attentive Transaction Embeddings for Next-item Recommendation
Shoujin Wang, Longbing Cao, Liang Hu +4
A transaction-based recommender system (TBRS) aims to predict the next item by modeling dependencies in transactional data. Generally, two kinds of dependencies considered are intr…
Stochastic Variance-Reduced Prox-Linear Algorithms for Nonconvex Composite Optimization
Junyu Zhang, Lin Xiao
We consider minimization of composite functions of the form , where and are convex functions (which can be nonsmooth) and is a smooth vector mapping. In a…
Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization
Hadrien Hendrikx, Lin Xiao, Sebastien Bubeck +2
We consider the setting of distributed empirical risk minimization where multiple machines compute the gradients in parallel and a centralized server updates the model parameters.…
Statistical Adaptive Stochastic Gradient Methods
Pengchuan Zhang, Hunter Lang, Qiang Liu +1
We propose a statistical adaptive procedure called SALSA for automatically scheduling the learning rate (step size) in stochastic gradient methods. SALSA first uses a smoothed stoc…
Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT
Tiankui Zhang, Yu Xu, Jonathan Loo +2
Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent concept, where a UAV equipped with a MEC server is deployed to serve a number of terminal d…
Variational Gram Functions: Convex Analysis and Optimization
Amin Jalali, Maryam Fazel, Lin Xiao
We propose a new class of convex penalty functions, called \emph{variational Gram functions} (VGFs), that can promote pairwise relations, such as orthogonality, among a set of vect…