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stat.ML2024
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…
cs.CR2024
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…
stat.ML2024★ 1 cited
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…