2 papers
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.ME2026
Identification and Inference for Structural Accelerated Failure Time Models via Instrument Interactions
Qiushi Bu, Wen Su, Xinyu Zhang +2
We study causal inference for time-to-event outcomes under right censoring in the presence of unmeasured confounding. Focusing on structural accelerated failure time models, we dev…