3 citations · 3 across the 6 of their papers we have counts for
7 papers
Wrong but Useful: Trajectory Value Beyond Answer Correctness in Multi-Agent Messages
Chih-Hsuan Yang, Anjir Ahmed Chowdhury, Cheng-Hau Yang +7
Multi-agent reasoning systems often use agreement, confidence, or automated scores to decide which messages should shape a final answer. Such filtering assumes that a message likel…
Sequential Inference for Gaussian Processes: A Signal Processing Perspective
Daniel Waxman, Fernando Llorente, Petar M. Djurić
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…
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…
Bayesian Ensembling: Insights from Online Optimization and Empirical Bayes
Daniel Waxman, Fernando Llorente, Petar M. Djurić
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…
Decentralized Online Ensembles of Gaussian Processes for Multi-Agent Systems
Fernando Llorente, Daniel Waxman, Petar M. Djurić
Flexible and scalable decentralized learning solutions are fundamentally important in the application of multi-agent systems. While several recent approaches introduce (ensembles o…
Fusion of Gaussian Processes Predictions with Monte Carlo Sampling
Marzieh Ajirak, Daniel Waxman, Fernando Llorente +1
In science and engineering, we often work with models designed for accurate prediction of variables of interest. Recognizing that these models are approximations of reality, it bec…