2 citations · 2 across the 8 of their papers we have counts for
8 papers · 1 filter
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…
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…
On the Benefits of Free Exploration for Regret Minimization in Multi-Armed Bandits
Yunlong Hou, Zixin Zhong, Vincent Y. F. Tan
We study a stochastic multi-armed bandit problem where an agent is granted a free exploration budget before regret accumulates, a setting not captured by the classic regret minimiz…
Demystifying the Slash Pattern in Attention: The Role of RoPE
Yuan Cheng, Fengzhuo Zhang, Yunlong Hou +5
Large Language Models (LLMs) often exhibit slash attention patterns, where attention scores concentrate along the -th sub-diagonal for some offset . These patterns play a key…
BanditSpec: Adaptive Speculative Decoding via Bandit Algorithms
Yunlong Hou, Fengzhuo Zhang, Cunxiao Du +6
Speculative decoding has emerged as a popular method to accelerate the inference of Large Language Models (LLMs) while retaining their superior text generation performance. Previou…
Almost Minimax Optimal Best Arm Identification in Piecewise Stationary Linear Bandits
Yunlong Hou, Vincent Y. F. Tan, Zixin Zhong
We propose a {\em novel} piecewise stationary linear bandit (PSLB) model, where the environment randomly samples a context from an unknown probability distribution at each changepo…
Probably Anytime-Safe Stochastic Combinatorial Semi-Bandits
Yunlong Hou, Vincent Y. F. Tan, Zixin Zhong
Motivated by concerns about making online decisions that incur undue amount of risk at each time step, in this paper, we formulate the probably anytime-safe stochastic combinatoria…
Almost Optimal Variance-Constrained Best Arm Identification
Yunlong Hou, Vincent Y. F. Tan, Zixin Zhong
We design and analyze VA-LUCB, a parameter-free algorithm, for identifying the best arm under the fixed-confidence setup and under a stringent constraint that the variance of the c…