5 papers
Meta-LinEXP3: Online-within-Online Learning for Adversarial Linear Contextual Bandits
Hao Li, Jie Xu, Zheng Xie
Meta-learning has emerged as an effective paradigm for transferring knowledge across sequential bandit tasks. While substantial progress has been made for stochastic bandits and no…
Converse and Collision-Based Achievability for Node Localization with Hybrid Distance-Spectral Graph Positional Encodings
Zimo Yan, Yifan Li, Hao Li +4
Graph positional encodings are widely used in graph neural networks and graph Transformers, yet it remains unclear when the code itself can identify nodes. We study a hybrid distan…
A Multi-Scale Graph Neural Process with Cross-Drug Co-Attention for Drug-Drug Interactions Prediction
Zimo Yan, Jie Zhang, Zheng Xie +2
Accurate prediction of drug-drug interactions (DDI) is crucial for medication safety and effective drug development. However, existing methods often struggle to capture structural…
Modified Meta-Thompson Sampling for Linear Bandits and Its Bayes Regret Analysis
Hao Li, Dong Liang, Zheng Xie
Meta-learning is characterized by its ability to learn how to learn, enabling the adaptation of learning strategies across different tasks. Recent research introduced the Meta-Thom…
Fast Sampling for Linear Inverse Problems of Vectors and Tensors using Multilinear Extensions
Hao Li, Dong Liang, Zixi Zhou +1
This paper studies the problem of sampling vector and tensor signals, which is the process of choosing sites in vectors and tensors to place sensors for better recovery. A small co…