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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 5SourcePDFScholar
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 38SourcePDFScholar