Scalable Meta-Learning with Gaussian Processes

no code implementations • 1 Dec 2023 • Petru Tighineanu, Lukas Grossberger, Paul Baireuther, Kathrin Skubch, Stefan Falkner, Julia Vinogradska, Felix Berkenkamp

Meta-learning is a powerful approach that exploits historical data to quickly solve new tasks from the same distribution.

Bayesian Optimization Gaussian Processes +1

Probabilistic Meta-Learning for Bayesian Optimization

no code implementations • 1 Jan 2021 • Felix Berkenkamp, Anna Eivazi, Lukas Grossberger, Kathrin Skubch, Jonathan Spitz, Christian Daniel, Stefan Falkner

Transfer and meta-learning algorithms leverage evaluations on related tasks in order to significantly speed up learning or optimization on a new problem.

Bayesian Optimization Meta-Learning +1

Bayesian Context Aggregation for Neural Processes

no code implementations • ICLR 2021 • Michael Volpp, Fabian Flürenbrock, Lukas Grossberger, Christian Daniel, Gerhard Neumann

Recently, casting probabilistic regression as a multi-task learning problem in terms of conditional latent variable (CLV) models such as the Neural Process (NP) has shown promising results.

Bayesian Inference Multi-Task Learning +1