3 citations · 3 across the 6 of their papers we have counts for
3 papers · 1 filter
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…