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stat.ML2026
Mini-Batch Covariance, Diffusion Limits, and Oracle Complexity in Stochastic Gradient Descent: A Sampling-Design Perspective
Daniel Zantedeschi, Kumar Muthuraman
Stochastic gradient descent (SGD) is central to simulation optimization, stochastic programming, and online M-estimation, where sampling effort is a decision variable. We study the…
stat.ML2026
Bayes with No Shame: Admissibility Geometries of Predictive Inference
Nicholas G. Polson, Daniel Zantedeschi
Modern predictive systems combine predictors, sequential monitors, prediction sets, and online strategies, each with a different certificate of optimality. We study four criterion-…