Bayesian Optimization and Design of Experiments
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Updated
May 24, 2024 - Python
Bayesian Optimization and Design of Experiments
Change-point detection and rate-monitoring for time-tagged event data using Bayesian Blocks (Scargle, 2013)
Computer-aided molecular and process design using Bayesian optimization
Symmetry crystal combinatorial optimization program for crystal prediction.
Gaussian Processes for Experimental Sciences
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
[ICML 2024] Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints
An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
MITIM (MIT Integrated Modeling) Suite for Fusion Applications
Generalized and Efficient Blackbox Optimization System
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Jointly-trained tree kernels for Gaussian processes
An evaluation framework for machine learning models simulating high-throughput materials discovery.
Pre-trained Gaussian processes for Bayesian optimization
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
Boax is a Bayesian Optimization library for JAX.
Scalable Non-myopic Bayesian Optimization in Dynamic Cost Settings
A package for the optimisation of numerical responses
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
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