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Petru Tighineanu

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…

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2022

Transfer Learning with Gaussian Processes for Bayesian Optimization

AISTATS 2022poster

Bayesian optimization is a powerful paradigm to optimize black-box functions based on scarce and noisy data. Its data efficiency can be further improved by transfer learning from related tasks. While recent transfer models meta-learn a prior based on large amount of data, in the low-data regime meth…