← Search

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…

Cited by 5SourcePDFScholar
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…