43 citations · 55 across the 20 of their papers we have counts for
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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…
Tighter Information-Theoretic Generalization Bounds from Supersamples
Ziqiao Wang, Yongyi Mao
In this work, we present a variety of novel information-theoretic generalization bounds for learning algorithms, from the supersample setting of Steinke & Zakynthinou (2020)-the se…