10 citations · 51 across the 18 of their papers we have counts for
4 papers · 1 filter
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…
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…