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Modal ⭐ 2,057

A modular active learning framework for Python

Gaussian Processes for Experimental Sciences

A Bayesian Back End for Design of Experiments

Experimental design and (multi-objective) bayesian optimization.

Differential Privacy Bayesian Optimization ⭐ 22

This repo contains the underlying code for all the experiments from the paper: "Automatic Discovery of Privacy-Utility Pareto Fronts"

Chemprop ⭐ 16

Fast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.

User-friendly Graphical User Interface (GUI) developed at the National Institute for Materials Science (NIMS) for performing statistical data analysis, machine learning (ML) modelisation, and composition/process optimisation through active learning assisted by Bayesian optimisation

This repository contains the code for the paper "Local policy search with Bayesian optimization".

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Python Bayesian Optimization (204)

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