14 papers
AdaFlash: Adaptive Speculative Decoding via On-Policy Distilled Diffusion Drafters
Yu-Yang Qian, Hao-Cong Wu, Chen Chen +4
Speculative decoding, in which a lightweight draft model first generates a draft sequence that is then verified in parallel by the target model, has become a prevalent paradigm for…
Nonlinear Bandit
Tianshuo Zheng, Ting Wu, Zhi-Hua Zhou +1
In this paper we first study the problem of generalized linear bandit (GLB) under heavy-tailed noise. The characteristics of heavy-tailed distributions are widely observed in real-…
TreeLoRA: Efficient Continual Learning via Layer-Wise LoRAs Guided by a Hierarchical Gradient-Similarity Tree
Yu-Yang Qian, Yuan-Ze Xu, Zhen-Yu Zhang +2
Many real-world applications collect data in a streaming environment, where learning tasks are encountered sequentially. This necessitates continual learning (CL) to update models…
Online Learning with Gradient-Variation Interval Regret
Yan-Feng Xie, Shuche Wang, Peng Zhao +1
This paper investigates non-stationary online learning using the metric of interval regret, which requires an online algorithm to perform well over every time interval. We propose…
Robust Length Prediction: A Perspective from Heavy-Tailed Prompt-Conditioned Distributions
Jing Wang, Yu-Yang Qian, Ke Xue +3
Output-length prediction is important for efficient LLM serving, as it directly affects batching, memory reservation, and scheduling. For prompt-only length prediction, most existi…
A Simple, Optimal and Efficient Algorithm for Online Exp-Concave Optimization
Yi-Han Wang, Peng Zhao, Zhi-Hua Zhou
Online eXp-concave Optimization (OXO) is a fundamental problem in online learning, where the goal is to minimize regret when loss functions are exponentially concave. The standard…