6 papers
Generalization Below the Edge of Stability: The Role of Data Geometry
Tongtong Liang, Alexander Cloninger, Rahul Parhi +1
Understanding generalization in overparameterized neural networks hinges on the interplay between the data geometry, neural architecture, and training dynamics. In this paper, we t…
Not-a-Bandit: Provably No-Regret Drafter Selection in Speculative Decoding for LLMs
Hongyi Liu, Jiaji Huang, Zhen Jia +2
Speculative decoding is widely used in accelerating large language model (LLM) inference. In this work, we focus on the online draft model selection problem in speculative decoding…
A second order regret bound for NormalHedge
Yoav Freund, Nicholas J. A. Harvey, Victor S. Portella +2
We consider the problem of prediction with expert advice for ``easy'' sequences. We show that a variant of NormalHedge enjoys a second-order -quantile regret bound of $O\big(\s…
Private-RAG: Answering Multiple Queries with LLMs while Keeping Your Data Private
Ruihan Wu, Erchi Wang, Zhiyuan Zhang +1
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by retrieving documents from an external corpus at inference time. When this corpus contains sensitive in…
DPCheatSheet: Using Worked and Erroneous LLM-usage Examples to Scaffold Differential Privacy Implementation
Shao-Yu Chu, Yuhe Tian, Yu-Xiang Wang +1
This paper explores how programmers without specialized expertise in differential privacy (DP) (i.e., novices) can leverage LLMs to implement DP programs with minimal training. We…
No-Regret Linear Bandits under Gap-Adjusted Misspecification
Chong Liu, Dan Qiao, Ming Yin +2
This work studies linear bandits under a new notion of gap-adjusted misspecification and is an extension of Liu et al. (2023). When the underlying reward function is not linear, ex…