5 citations · 6 across the 8 of their papers we have counts for
10 papers
Quality-Aware Cross-Model Computation Reuse
Jin Cheng, Xiangxiang Dai, Maoli Liu +3
An intermediate result computed by one model can be reused by other models to perform their tasks. Existing work mainly focuses on practical execution, leaving a theoretical gap in…
Quantum Multi-Armed Bandits and Linear Bandits: Lower Bounds and Algorithms
Maoli Liu, Zhuohua Li, John C. S. Lui
We study quantum multi-armed bandits (QMAB) and quantum linear bandits (QLB), where the learner queries each arm or action through a quantum reward oracle or its inverse. Prior wor…
Tokenizing Numerical and Embedding Features for LLM RecSys
Zhe Xu, Ankit Peshin, Chiyu Zhang +7
Large language models (LLMs) are increasingly used as backbone architectures for recommender systems because of their strong sequence modeling and representation learning capabilit…
Trading Vector Data in Vector Databases
Jin Cheng, Xiangxiang Dai, Ningning Ding +2
Vector data trading is essential for cross-domain learning with vector databases, yet it remains largely unexplored. We study this problem under online learning, where sellers face…
Learning Best Paths in Quantum Networks
Xuchuang Wang, Maoli Liu, Xutong Liu +4
Quantum networks (QNs) transmit delicate quantum information across noisy quantum channels. Crucial applications, like quantum key distribution (QKD) and distributed quantum comput…
Leveraging the Power of Conversations: Optimal Key Term Selection in Conversational Contextual Bandits
Maoli Liu, Zhuohua Li, Xiangxiang Dai +1
Conversational recommender systems proactively query users with relevant "key terms" and leverage the feedback to elicit users' preferences for personalized recommendations. Conver…
A Multi-Agent Conversational Bandit Approach to Online Evaluation and Selection of User-Aligned LLM Responses
Xiangxiang Dai, Yuejin Xie, Maoli Liu +4
Prompt-based offline methods are commonly used to optimize large language model (LLM) responses, but evaluating these responses is computationally intensive and often fails to acco…
Demystifying Online Clustering of Bandits: Enhanced Exploration Under Stochastic and Smoothed Adversarial Contexts
Zhuohua Li, Maoli Liu, Xiangxiang Dai +1
The contextual multi-armed bandit (MAB) problem is crucial in sequential decision-making. A line of research, known as online clustering of bandits, extends contextual MAB by group…
FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients
Zhuohua Li, Maoli Liu, John C. S. Lui
Conversational recommender systems have emerged as a potent solution for efficiently eliciting user preferences. These systems interactively present queries associated with "key te…
Dynamic Quantum Group Key Agreement via Tree Key Graphs
Qiang Zhao, Zhuohua Li, John C. S. Lui
Quantum key distribution (QKD) protocols are essential to guarantee information-theoretic security in quantum communication. Although there was some previous work on quantum group…