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cs.LG2025
Private Online Learning against an Adaptive Adversary: Realizable and Agnostic Settings
Bo Li, Wei Wang, Peng Ye
We revisit the problem of private online learning, in which a learner receives a sequence of data points and has to respond at each time-step a hypothesis. It is required that…
stat.ML2025
Private Realizable-to-Agnostic Transformation with Near-Optimal Sample Complexity
Bo Li, Wei Wang, Peng Ye
The realizable-to-agnostic transformation (Beimel et al., 2015; Alon et al., 2020) provides a general mechanism to convert a private learner in the realizable setting (where the ex…
cs.CL2025
Enhancing LLM Safety Through a Theoretical Minimax Game Lens
Yihe Deng, Yu Yang, Junkai Zhang +2
The rapid advancement of large language models (LLMs) necessitates effective mechanisms to ensure their responsible deployment by accurately distinguishing unsafe content from beni…