4 citations · 4 across the 6 of their papers we have counts for
7 papers · 1 filter
Bayesian Fixed-Confidence Best-Arm Identification with Abstention
Yuqi Huang, Yunlong Hou, Vincent Y. F. Tan
We study the Bayesian fixed-confidence best-arm identification problem in which a learner can abstain from making a terminal recommendation. Subject to a target error probability $…
SPec: Adaptive Self-Speculative Decoding for Vision-Language Models
Yuqi Huang, Xingyao Li, Yunlong Hou +3
Speculative Decoding (SD) has demonstrated its efficacy in accelerating large language model inference, but for Vision-Language Models (VLMs), the diverse input modalities make it…
StableJudge: A Lightweight Adversarial Defense Framework for LLM-as-a-Judge
Anning Hu, Yangyu Li, Yuqi Huang +2
Large language models (LLMs) are increasingly used as judges to assign scalar scores that drive downstream decision-making, including tasks such as paper review and recommendation.…
Balancing Expressivity and Learnability in Quantum Kernel Bandit Optimization
Yuqi Huang, Vincent Y. F. Tan, Sharu Theresa Jose
We investigate Gaussian process (GP) bandit optimization with quantum kernels, assuming the mean reward function lies in the reproducing kernel Hilbert space (RKHS) induced by the…
Bayesian Best-Arm Identification with Abstention: A Polynomial-to-Exponential Phase Transition
Yuqi Huang, Yunlong Hou, Vincent Y. F. Tan
We study the Bayesian fixed-budget best-arm identification problem in which a learner can abstain from making a terminal recommendation. Subject to an abstention budget , we ana…
REDSearcher: A Scalable and Cost-Efficient Framework for Long-Horizon Search Agents
Zheng Chu, Xiao Wang, Jack Hong +11
Large language models are transitioning from generalpurpose knowledge engines to realworld problem solvers, yet optimizing them for deep search tasks remains challenging. The centr…
Quantum-Enhanced Neural Contextual Bandit Algorithms
Yuqi Huang, Vincent Y. F Tan, Sharu Theresa Jose
Stochastic contextual bandits are fundamental for sequential decision-making but pose significant challenges for existing neural network-based algorithms, particularly when scaling…