47 citations · 187 across the 32 of their papers we have counts for
9 papers · 1 filter
From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems
Yao Zhou, Haonan Wang, Jingrui He +1
With the prevalence of deep learning based embedding approaches, recommender systems have become a proven and indispensable tool in various information filtering applications. Howe…
Deeper-GXX: Deepening Arbitrary GNNs
Lecheng Zheng, Dongqi Fu, Ross Maciejewski +1
Recently, motivated by real applications, a major research direction in graph neural networks (GNNs) is to explore deeper structures. For instance, the graph connectivity is not al…
Deep Active Learning by Leveraging Training Dynamics
Haonan Wang, Wei Huang, Ziwei Wu +3
Active learning theories and methods have been extensively studied in classical statistical learning settings. However, deep active learning, i.e., active learning with deep learni…
EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits
Yikun Ban, Yuchen Yan, Arindam Banerjee +1
In this paper, we propose a novel neural exploration strategy in contextual bandits, EE-Net, distinct from the standard UCB-based and TS-based approaches. Contextual multi-armed ba…
Convolutional Neural Bandit for Visual-aware Recommendation
Yikun Ban, Jingrui He
Online recommendation/advertising is ubiquitous in web business. Image displaying is considered as one of the most commonly used formats to interact with customers. Contextual mult…
DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Data
Dongqi Fu, Jingrui He
In the big data era, the relationship between entries becomes more and more complex. Many graph (or network) algorithms have already paid attention to dynamic networks, which are m…
Multi-facet Contextual Bandits: A Neural Network Perspective
Yikun Ban, Jingrui He, Curtiss B. Cook
Contextual multi-armed bandit has shown to be an effective tool in recommender systems. In this paper, we study a novel problem of multi-facet bandits involving a group of bandits,…
Local Clustering in Contextual Multi-Armed Bandits
Yikun Ban, Jingrui He
We study identifying user clusters in contextual multi-armed bandits (MAB). Contextual MAB is an effective tool for many real applications, such as content recommendation and onlin…
Deep Co-Attention Network for Multi-View Subspace Learning
Lecheng Zheng, Yu Cheng, Hongxia Yang +2
Many real-world applications involve data from multiple modalities and thus exhibit the view heterogeneity. For example, user modeling on social media might leverage both the topol…