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stat.ML2026
Gradient-Flow Optimization as Dynamic Random-Effects Inference: Testing and Early Stopping with Applications to Deep Learning
Minhao Yao, Ruoyu Wang, Xihong Lin +2
Gradient-flow optimization is usually viewed as an algorithmic procedure for minimizing empirical loss, with training duration selected by validation or heuristic early stopping ru…
stat.ML2026
Harnessing Synthetic Data from Generative AI for Statistical Inference
Ahmad Abdel-Azim, Ruoyu Wang, Xihong Lin
The emergence of generative AI models has dramatically expanded the availability and use of synthetic data across scientific, industrial, and policy domains. While these developmen…
stat.ML2026
"Rebuilding" Statistics in the Age of AI: A Town Hall Discussion on Culture, Infrastructure, and Training
David L. Donoho, Jian Kang, Xihong Lin +7
This article presents the full, original record of the 2024 Joint Statistical Meetings (JSM) town hall, "Statistics in the Age of AI," which convened leading statisticians to discu…