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stat.ML2026
Two-Sample Testing via Path-based Inference
Eshant English, Wei-Cheng Lai, Yanfeng Yang +3
Modern deep generative models are primarily studied for their ability to generate realistic samples, yet the generative dynamics they learn can also serve as objects of statistical…
stat.ML2026
Zero-Flow Encoders
Yakun Wang, Leyang Wang, Song Liu +1
Flow-based methods have achieved significant success in various generative modeling tasks, capturing nuanced details within complex data distributions. However, few existing works…
stat.ML2025
In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning
Tomoya Wakayama, Taiji Suzuki
This paper develops a finite-sample statistical theory for in-context learning (ICL), analyzed within a meta-learning framework that accommodates mixtures of diverse task types. We…