# GPyOpt Projects at AI Tinkerers

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> Technology record last updated: 2026-02-25T10:19:16Z
> Generated: 2026-09-23T09:32:14Z

GPyOpt is a Python-based Bayesian optimization library built on Gaussian processes to efficiently solve expensive black-box tuning problems.

GPyOpt leverages the GPy framework to automate global optimization for functions where evaluations are computationally expensive (such as hyperparameter tuning for deep learning). It supports a wide range of acquisition functions: Expected Improvement (EI), Probability of Improvement (PI), and Lower Confidence Bound (LCB). The library manages complex input spaces including continuous, discrete, and categorical variables. By utilizing batch evaluations and parallel processing, GPyOpt helps researchers find optimal configurations with fewer iterations than standard grid or random search methods.

- Official technology site: https://github.com/SheffieldML/GPyOpt
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [AutoRAG](https://berlin.aitinkerers.org/talks/rsvp_4eSc1tq148s)

Automatic hyperparameter optimization of your retrieval augmented generation pipeline. Stop fiddling around manually with chunk size, top k and so on. Let the machine do the tuning of your retrieval settings. Inspired by hyperparameter optimization in conventional ML.

- Event context: AI Tinkerers Berlin - March 21 — 2024-03-21 — Berlin
- Public talk page: https://berlin.aitinkerers.org/talks/rsvp_4eSc1tq148s

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