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Lukas Grossberger

2 accepted papers

2024

Scalable Meta-Learning with Gaussian Processes

AISTATS 2024poster

Meta-learning is a powerful approach that exploits historical data to quickly solve new tasks from the same distribution. In the low-data regime, methods based on the closed-form posterior of Gaussian processes (GP) together with Bayesian optimization have achieved high performance. However, these m…

Cited by 5Source ↗PDF ↗Scholar ↗
2021

Bayesian Context Aggregation for Neural Processes

ICLR 2021poster

Formulating scalable probabilistic regression models with reliable uncertainty estimates has been a long-standing challenge in machine learning research. Recently, casting probabilistic regression as a multi-task learning problem in terms of conditional latent variable (CLV) models such as the Neur…

Cited by 38Source ↗PDF ↗Scholar ↗