5 papers
Sequential Inference for Gaussian Processes: A Signal Processing Perspective
Daniel Waxman, Fernando Llorente, Petar M. DjuriÄ
The proliferation of capable and efficient machine learning (ML) models marks one of the strongest methodological shifts in signal processing (SP) in its nearly 100-year history. M…
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…
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…
Robust, Online, and Adaptive Decentralized Gaussian Processes
Fernando Llorente, Daniel Waxman, Sanket Jantre +2
Gaussian processes (GPs) offer a flexible, uncertainty-aware framework for modeling complex signals, but scale cubically with data, assume static targets, and are brittle to outlie…
Decentralized Online Ensembles of Gaussian Processes for Multi-Agent Systems
Fernando Llorente, Daniel Waxman, Petar M. DjuriÄ
Flexible and scalable decentralized learning solutions are fundamentally important in the application of multi-agent systems. While several recent approaches introduce (ensembles o…