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09/21/2023
Incentivized Communication for Federated Bandits
Most existing works on federated bandits take it for granted that all cl...
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06/13/2023
Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial?
The past decade has witnessed the flourishing of a new profession as med...
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03/11/2023
Uncertainty-Aware Off-Policy Learning
Off-policy learning, referring to the procedure of policy optimization w...
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02/15/2023
Meta-Reinforcement Learning via Exploratory Task Clustering
Meta-reinforcement learning (meta-RL) aims to quickly solve new tasks by...
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02/03/2023
How Bad is Top-K Recommendation under Competing Content Creators?
Content creators compete for exposure on recommendation platforms, and s...
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01/13/2023
Disentangled Representation for Diversified Recommendations
Accuracy and diversity have long been considered to be two conflicting g...
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11/06/2022
Integrating Voice-Based Machine Learning Technology into Complex Home Environments
To demonstrate the value of machine learning based smart health technolo...
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11/06/2022
MiddleGAN: Generate Domain Agnostic Samples for Unsupervised Domain Adaptation
In recent years, machine learning has achieved impressive results across...
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10/14/2022
COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation
Personalized text generation has broad industrial applications, such as ...
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10/02/2022
Spectral Augmentation for Self-Supervised Learning on Graphs
Graph contrastive learning (GCL), as an emerging self-supervised learnin...
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08/31/2022
Rethinking Conversational Recommendations: Is Decision Tree All You Need?
Conversational recommender systems (CRS) dynamically obtain the user pre...
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08/30/2022
Dynamic Global Sensitivity for Differentially Private Contextual Bandits
Bandit algorithms have become a reference solution for interactive recom...
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07/10/2022
Not Just Skipping. Understanding the Effect of Sponsored Content on Users' Decision-Making in Online Health Search
Advertisements (ads) are an innate part of search engine business models...
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06/13/2022
Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback
Deep neural networks (DNNs) demonstrate significant advantages in improv...
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06/10/2022
Communication Efficient Distributed Learning for Kernelized Contextual Bandits
We tackle the communication efficiency challenge of learning kernelized ...
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05/24/2022
Meta Policy Learning for Cold-Start Conversational Recommendation
Conversational recommender systems (CRS) explicitly solicit users' prefe...
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02/20/2022
Graph-based Extractive Explainer for Recommendations
Explanations in a recommender system assist users in making informed dec...
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02/03/2022
Learning from a Learning User for Optimal Recommendations
In real-world recommendation problems, especially those with a formidabl...
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02/02/2022
Communication Efficient Federated Learning for Generalized Linear Bandits
Contextual bandit algorithms have been recently studied under the federa...
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01/24/2022
The Enforced Transfer: A Novel Domain Adaptation Algorithm
Existing Domain Adaptation (DA) algorithms train target models and then ...
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01/24/2022
Learning Contextual Bandits Through Perturbed Rewards
Thanks to the power of representation learning, neural contextual bandit...
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01/24/2022
IMO^3: Interactive Multi-Objective Off-Policy Optimization
Most real-world optimization problems have multiple objectives. A system...
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01/17/2022
Learning Neural Ranking Models Online from Implicit User Feedback
Existing online learning to rank (OL2R) solutions are limited to linear ...
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11/01/2021
Calibrating Explore-Exploit Trade-off for Fair Online Learning to Rank
Online learning to rank (OL2R) has attracted great research interests in...
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11/01/2021
Graph Structural Attack by Spectral Distance
Graph Convolutional Networks (GCNs) have fueled a surge of interest due ...
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11/01/2021
Comparative Explanations of Recommendations
As recommendation is essentially a comparative (or ranking) process, a g...
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10/31/2021
Graph Embedding with Hierarchical Attentive Membership
The exploitation of graph structures is the key to effectively learning ...
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10/26/2021
Unbiased Graph Embedding with Biased Graph Observations
Graph embedding techniques have been increasingly employed in real-world...
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10/18/2021
When Are Linear Stochastic Bandits Attackable?
We study adversarial attacks on linear stochastic bandits, a sequential ...
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10/06/2021
Learning the Optimal Recommendation from Explorative Users
We propose a new problem setting to study the sequential interactions be...
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10/04/2021
Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits
Linear contextual bandit is a popular online learning problem. It has be...
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07/22/2021
Improve Learning from Crowds via Generative Augmentation
Crowdsourcing provides an efficient label collection schema for supervis...
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04/14/2021
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution
Collaborative bandit learning, i.e., bandit algorithms that utilize coll...
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04/08/2021
Incentivizing Exploration in Linear Bandits under Information Gap
We study the problem of incentivizing exploration for myopic users in li...
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02/28/2021
PairRank: Online Pairwise Learning to Rank by Divide-and-Conquer
Online Learning to Rank (OL2R) eliminates the need of explicit relevance...
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02/14/2021
Reversible Action Design for Combinatorial Optimization with Reinforcement Learning
Combinatorial optimization problem (COP) over graphs is a fundamental ch...
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01/24/2021
Explanation as a Defense of Recommendation
Textual explanations have proved to help improve user satisfaction on ma...
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12/24/2020
Learning from Crowds by Modeling Common Confusions
Crowdsourcing provides a practical way to obtain large amounts of labele...
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09/05/2020
Unifying Clustered and Non-stationary Bandits
Non-stationary bandits and online clustering of bandits lift the restric...
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06/09/2020
Directional Multivariate Ranking
User-provided multi-aspect evaluations manifest users' detailed feedback...
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05/18/2020
Unbiased Learning to Rank via Propensity Ratio Scoring
Implicit feedback, such as user clicks, is a major source of supervision...
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01/29/2020
Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation
Predicting users' preferences based on their sequential behaviors in his...
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12/01/2019
JNET: Learning User Representations via Joint Network Embedding and Topic Embedding
User representation learning is vital to capture diverse user preference...
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11/10/2019
Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation
Reinforcement learning is effective in optimizing policies for recommend...
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11/10/2019
A Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation
Reinforcement learning is effective in optimizing policies for recommend...
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09/18/2019
BPMR: Bayesian Probabilistic Multivariate Ranking
Multi-aspect user preferences are attracting wider attention in recommen...
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09/02/2019
Active Collaborative Sensing for Energy Breakdown
Residential homes constitute roughly one-fourth of the total energy usag...
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08/24/2019
Adversarial Domain Adaptation for Machine Reading Comprehension
In this paper, we focus on unsupervised domain adaptation for Machine Re...
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06/10/2019
Variance Reduction in Gradient Exploration for Online Learning to Rank
Online Learning to Rank (OL2R) algorithms learn from implicit user feedb...
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06/09/2019
