2 papers
stat.ML2026
Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning
Pietro Carlotti, Nevena GligiÄ, 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…
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…