Search Results for author:
Found 16 papers, 5 papers with code
Neural Active Learning Beyond Bandits
We study both stream-based and pool-based active learning with neural network approximations.
Logic Query of Thoughts: Guiding Large Language Models to Answer Complex Logic Queries with Knowledge Graphs
Through the utilization of both knowledge graph reasoning and LLMs, it successfully derives answers for each subquestion.
Neural Contextual Bandits for Personalized Recommendation
Different from existing related tutorials, (1) we focus on the exploration perspective of contextual bandits to alleviate the ``Matthew Effect'' in the recommender systems, i. e., the rich get richer and the poor get poorer, concerning the popularity of items; (2) in addition to the conventional linear contextual bandits, we will also dedicated to neural contextual bandits which have emerged as an important branch in recent years, to investigate how neural networks benefit contextual bandits for personalized recommendation both empirically and theoretically; (3) we will cover the latest topic, collaborative neural contextual bandits, to incorporate both user heterogeneity and user correlations customized for recommender system; (4) we will provide and discuss the new emerging challenges and open questions for neural contextual bandits with applications in the personalized recommendation, especially for large neural models.
Contextual Bandits with Online Neural Regression
Based on such a perturbed prediction, we show a ${\mathcal{O}}(\log T)$ regret for online regression with both squared loss and KL loss, and subsequently convert these respectively to $\tilde{\mathcal{O}}(\sqrt{KT})$ and $\tilde{\mathcal{O}}(\sqrt{KL^*} + K)$ regret for NeuCB, where $L^*$ is the loss of the best policy.
Graph Neural Bandits
Contextual bandits algorithms aim to choose the optimal arm with the highest reward out of a set of candidates based on the contextual information.
Neural Exploitation and Exploration of Contextual Bandits
In recent literature, a series of neural bandit algorithms have been proposed to adapt to the non-linear reward function, combined with TS or UCB strategies for exploration.
Improved Algorithms for Neural Active Learning
We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting.
Neural Bandit with Arm Group Graph
Contextual bandits aim to identify among a set of arms the optimal one with the highest reward based on their contextual information.
DISCO: Comprehensive and Explainable Disinformation Detection
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 issues, such as political agendas and manipulating financial markets.
Neural Collaborative Filtering Bandits via Meta Learning
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
To overcome this challenge, a series of neural bandit algorithms have been proposed, where a neural network is used to learn the underlying reward function and TS or UCB are adapted for exploration.
Convolutional Neural Bandit for Visual-aware Recommendation
Online recommendation/advertising is ubiquitous in web business.
Multi-facet Contextual Bandits: A Neural Network Perspective
In this paper, we study a novel problem of multi-facet bandits involving a group of bandits, each characterizing the users' needs from one unique aspect.
Local Clustering in Contextual Multi-Armed Bandits
We study identifying user clusters in contextual multi-armed bandits (MAB).
Generic Outlier Detection in Multi-Armed Bandit
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, healthcare, and online advertising.
Catching Loosely Synchronized Behavior in Face of Camouflage
Fraud has severely detrimental impacts on the business of social networks and other online applications.
Social and Information Networks Cryptography and Security
