10 citations · 62 across the 25 of their papers we have counts for
18 papers · 1 filter
GCL-OT: Graph Contrastive Learning with Optimal Transport for Heterophilic Text-Attributed Graphs
Yating Ren, Yikun Ban, Huobin Tan
Recently, structure-text contrastive learning has shown promising performance on text-attributed graphs by leveraging the complementary strengths of graph neural networks and langu…
Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision?
Zihao Li, Lecheng Zheng, Bowen Jin +5
While great success has been achieved in building vision models with Contrastive Language-Image Pre-training (CLIP) over internet-scale image-text pairs, building transferable Grap…
PageRank Bandits for Link Prediction
Yikun Ban, Jiaru Zou, Zihao Li +5
Link prediction is a critical problem in graph learning with broad applications such as recommender systems and knowledge graph completion. Numerous research efforts have been dire…
LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation
Xinrui He, Yikun Ban, Jiaru Zou +3
Missing data imputation is a critical challenge in various domains, such as healthcare and finance, where data completeness is vital for accurate analysis. Large language models (L…
Meta Clustering of Neural Bandits
Yikun Ban, Yunzhe Qi, Tianxin Wei +2
The contextual bandit has been identified as a powerful framework to formulate the recommendation process as a sequential decision-making process, where each item is regarded as an…
Neural Active Learning Beyond Bandits
Yikun Ban, Ishika Agarwal, Ziwei Wu +4
We study both stream-based and pool-based active learning with neural network approximations. A recent line of works proposed bandit-based approaches that transformed active learni…
Contextual Bandits with Online Neural Regression
Rohan Deb, Yikun Ban, Shiliang Zuo +2
Recent works have shown a reduction from contextual bandits to online regression under a realizability assumption [Foster and Rakhlin, 2020, Foster and Krishnamurthy, 2021]. In thi…
Graph Neural Bandits
Yunzhe Qi, Yikun Ban, Jingrui He
Contextual bandits algorithms aim to choose the optimal arm with the highest reward out of a set of candidates based on the contextual information. Various bandit algorithms have b…
Neural Exploitation and Exploration of Contextual Bandits
Yikun Ban, Yuchen Yan, Arindam Banerjee +1
In this paper, we study utilizing neural networks for the exploitation and exploration of contextual multi-armed bandits. Contextual multi-armed bandits have been studied for decad…
Improved Algorithms for Neural Active Learning
Yikun Ban, Yuheng Zhang, Hanghang Tong +2
We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting. In particular, we introduce tw…
Neural Bandit with Arm Group Graph
Yunzhe Qi, Yikun Ban, Jingrui He
Contextual bandits aim to identify among a set of arms the optimal one with the highest reward based on their contextual information. Motivated by the fact that the arms usually ex…
DISCO: Comprehensive and Explainable Disinformation Detection
Dongqi Fu, Yikun Ban, Hanghang Tong +2
Disinformation refers to false information deliberately spread to influence the general public, and the negative impact of disinformation on society can be observed in numerous iss…
Neural Collaborative Filtering Bandits via Meta Learning
Yikun Ban, Yunzhe Qi, Tianxin Wei +1
Contextual multi-armed bandits provide powerful tools to solve the exploitation-exploration dilemma in decision making, with direct applications in the personalized recommendation.…
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…
Generic Outlier Detection in Multi-Armed Bandit
Yikun Ban, Jingrui He
In this paper, we study the problem of outlier arm detection in multi-armed bandit settings, which finds plenty of applications in many high-impact domains such as finance, healthc…