19 Repositories
Latest Python Libraries
Bayesian Optimization using GPflow
Note: This package is for use with GPFlow 1. For Bayesian optimization using GPFlow 2 please see Trieste, a joint effort with Secondmind. GPflowOpt GP
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
OCTIS : Optimizing and Comparing Topic Models is Simple! OCTIS (Optimizing and Comparing Topic models Is Simple) aims at training, analyzing and compa
Generalized and Efficient Blackbox Optimization System.
OpenBox Doc | OpenBox中文文档 OpenBox: Generalized and Efficient Blackbox Optimization System OpenBox is an efficient and generalized blackbox optimizatio
TensorFlow implementation for Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How
Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How TensorFlow implementation for Bayesian Modeling and Unce
Fast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.
Evidential Deep Learning for Guided Molecular Property Prediction and Discovery Ava Soleimany*, Alexander Amini*, Samuel Goldman*, Daniela Rus, Sangee
Python implementation of Bayesian optimization over permutation spaces.
Bayesian Optimization over Permutation Spaces This repository contains the source code and the resources related to the paper "Bayesian Optimization o
Distributed Asynchronous Hyperparameter Optimization better than HyperOpt.
UltraOpt : Distributed Asynchronous Hyperparameter Optimization better than HyperOpt. UltraOpt is a simple and efficient library to minimize expensive
Sequential Model-based Algorithm Configuration
SMAC v3 Project Copyright (C) 2016-2018 AutoML Group Attention: This package is a reimplementation of the original SMAC tool (see reference below). Ho
Automated Machine Learning with scikit-learn
auto-sklearn auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator. Find the documentation here
Large scale and asynchronous Hyperparameter Optimization at your fingertip.
Syne Tune This package provides state-of-the-art distributed hyperparameter optimizers (HPO) where trials can be evaluated with several backend option
A modular active learning framework for Python
Modular Active Learning framework for Python3 Page contents Introduction Active learning from bird's-eye view modAL in action From zero to one in a fe
A Python implementation of global optimization with gaussian processes.
Bayesian Optimization Pure Python implementation of bayesian global optimization with gaussian processes. PyPI (pip): $ pip install bayesian-optimizat
Simple, but essential Bayesian optimization package
BayesO: A Bayesian optimization framework in Python Simple, but essential Bayesian optimization package. http://bayeso.org Online documentation Instal
A modular active learning framework for Python
Modular Active Learning framework for Python3 Page contents Introduction Active learning from bird's-eye view modAL in action From zero to one in a fe
Dynamica causal Bayesian optimisation
Dynamic Causal Bayesian Optimization This is a Python implementation of Dynamic Causal Bayesian Optimization as presented at NeurIPS 2021. Abstract Th
This repository contains Prior-RObust Bayesian Optimization (PROBO) as introduced in our paper "Accounting for Gaussian Process Imprecision in Bayesian Optimization"
Prior-RObust Bayesian Optimization (PROBO) Introduction, TOC This repository contains Prior-RObust Bayesian Optimization (PROBO) as introduced in our
Sequential model-based optimization with a `scipy.optimize` interface
Scikit-Optimize Scikit-Optimize, or skopt, is a simple and efficient library to minimize (very) expensive and noisy black-box functions. It implements
Optimising chemical reactions using machine learning
Summit Summit is a set of tools for optimising chemical processes. We’ve started by targeting reactions. What is Summit? Currently, reaction optimisat
AutoOED: Automated Optimal Experiment Design Platform
AutoOED is an optimal experiment design platform powered with automated machine learning to accelerate the discovery of optimal solutions. Our platform solves multi-objective optimization problems an