1 citations · 1 across the 7 of their papers we have counts for
8 papers
Fundamental Trade-Offs in Multi-Bit Watermarking of Stochastic Processes
Haiyun He, Yepeng Liu, Zhuoer Shen +3
We study multi-bit watermarking for data generated by stochastic processes, where a hidden message is embedded during sampling and must be decodable by an authorized detector that…
On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective
Zhiyi Dong, Zixuan Liu, Yongyi Mao
This paper studies the hardness of unsupervised domain adaptation (UDA) under covariate shift. We model the uncertainty that the learner faces by a distribution in the ground-t…
Generalization in Federated Learning: A Conditional Mutual Information Framework
Ziqiao Wang, Cheng Long, Yongyi Mao
Federated learning (FL) is a widely adopted privacy-preserving distributed learning framework, yet its generalization performance remains less explored compared to centralized lear…
Distributional Information Embedding: A Framework for Multi-bit Watermarking
Haiyun He, Yepeng Liu, Ziqiao Wang +2
This paper introduces a novel problem, distributional information embedding, motivated by the practical demands of multi-bit watermarking for large language models (LLMs). Unlike t…
Generalization Bounds via Conditional -Information
Ziqiao Wang, Yongyi Mao
In this work, we introduce novel information-theoretic generalization bounds using the conditional -information framework, an extension of the traditional conditional mutual inf…
Theoretically Grounded Framework for LLM Watermarking: A Distribution-Adaptive Approach
Haiyun He, Yepeng Liu, Ziqiao Wang +2
Watermarking has emerged as a crucial method to distinguish AI-generated text from human-created text. Current watermarking approaches often lack formal optimality guarantees or ad…
On -Divergence Principled Domain Adaptation: An Improved Framework
Ziqiao Wang, Yongyi Mao
Unsupervised domain adaptation (UDA) plays a crucial role in addressing distribution shifts in machine learning. In this work, we improve the theoretical foundations of UDA propose…
Sample-Conditioned Hypothesis Stability Sharpens Information-Theoretic Generalization Bounds
Ziqiao Wang, Yongyi Mao
We present new information-theoretic generalization guarantees through the a novel construction of the "neighboring-hypothesis" matrix and a new family of stability notions termed…