5 papers · 1 filter
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…
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…
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…