3 papers
stat.ME2026
Product Monte Carlo: Expectation Under a Product of Densities
Pietro Carlotti, Sifan Liu, Yuling Yao
Aggregating independently computed sub-sample inferences is a well-known challenge in large-scale Bayesian computation, federated learning, and opinion pooling. The core problem is…
stat.ME2026
A Bayesian Critique of Rank-Based Methods for Surrogate Marker Evaluation
Pietro Carlotti, Layla Parast
Surrogate markers are often employed in clinical trials to replace primary outcomes that may be difficult, expensive, or time-consuming to measure directly. These markers can accel…
stat.ML2026
Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning
Pietro Carlotti, Nevena Gligić, Arya Farahi
Evidential Deep Learning (EDL) is a popular framework for uncertainty-aware classification that models predictive uncertainty via Dirichlet distributions parameterized by neural ne…