activity
20192022
most citedJointly Modeling Intra- and Inter-transaction Dependencies with Hierarchical Attentive Transaction Embeddings for Next-item Recommendation

30 citations · 63 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CL20221 cited

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…

cs.GT20222 cited

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…

cs.CL20214 cited

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…

math.OC20202 cited

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…

cs.IR202030 cited

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…

math.OC202016 cited

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.…