activity
20152026
most citedCascading Bandits: Learning to Rank in the Cascade Model

108 citations · 193 across the 16 of their papers we have counts for

collaborators

27 papers

cs.LG20267 cited

Budgeted Online Influence Maximization

Pierre Perrault, Jennifer Healey, Zheng Wen +1

We introduce a new budgeted framework for online influence maximization, considering the total cost of an advertising campaign instead of the common cardinality constraint on a cho…

stat.ML2022

Evaluating High-Order Predictive Distributions in Deep Learning

Ian Osband, Zheng Wen, Seyed Mohammad Asghari +3

Most work on supervised learning research has focused on marginal predictions. In decision problems, joint predictive distributions are essential for good performance. Previous wor…

cs.LG20211 cited

Joint Online Learning and Decision-making via Dual Mirror Descent

Alfonso Lobos, Paul Grigas, Zheng Wen

We consider an online revenue maximization problem over a finite time horizon subject to lower and upper bounds on cost. At each period, an agent receives a context vector sampled…

cs.LG202018 cited

Neural Contextual Bandits with Deep Representation and Shallow Exploration

Pan Xu, Zheng Wen, Handong Zhao +1

We study a general class of contextual bandits, where each context-action pair is associated with a raw feature vector, but the reward generating function is unknown. We propose a…

cs.CV20205 cited

A Benchmark and Baseline for Language-Driven Image Editing

Jing Shi, Ning Xu, Trung Bui +3

Language-driven image editing can significantly save the laborious image editing work and be friendly to the photography novice. However, most similar work can only deal with a spe…

cs.LG20204 cited

On the Sample Complexity of Reinforcement Learning with Policy Space Generalization

Wenlong Mou, Zheng Wen, Xi Chen

We study the optimal sample complexity in large-scale Reinforcement Learning (RL) problems with policy space generalization, i.e. the agent has a prior knowledge that the optimal p…