2 papers
cs.LG2026
Bayesian Ensembling: Insights from Online Optimization and Empirical Bayes
Daniel Waxman, Fernando Llorente, Petar M. DjuriÄ
We revisit the classical problem of Bayesian ensembles and address the challenge of learning optimal combinations of Bayesian models in an online, continual learning setting. To th…
stat.ME2026
Designing an Optimal Sensor Network via Minimizing Information Loss
Daniel Waxman, Fernando Llorente, Katia Lamer +1
Optimal experimental design is a classic topic in statistics, with many well-studied problems, applications, and solutions. The design problem we study is the placement of sensors…