IEEE

Scalable, Federated Gaussian Process Training for Decentralized Multi-Agent Systems

Abstract: Gaussian process (GP) training of kernel hyperparameters still remains a major challenge due to high computational complexity. The typical GP training method employs maximum likelihood ...
IEEE

Gaussian-Cauchy Mixture Kernel Function Based Maximum Correntropy Criterion Kalman Filter for Linear Non-Gaussian Systems

Abstract: This paper proposes a Gaussian-Cauchy mixture maximum correntropy criterion Kalman filter algorithm (GCM_MCCKF) for robust state estimation in linear systems under non-Gaussian noise, ...
eLife

Learning sequence-function relationships with scalable, interpretable Gaussian processes

This important work introduces a family of interpretable Gaussian process models that allows us to learn and model sequence-function relationships in biomolecules. These models are applied to three ...