Alternatives and similar repositories for bayesian_optimization

Users that are interested in bayesian_optimization are comparing it to the libraries listed below

• jmetzen / gp_extras
  Additional kernels that can be used with scikit-learn's Gaussian Process module
  ☆82Updated last year
• UCL-SML / Doubly-Stochastic-DGP
  Deep Gaussian Processes with Doubly Stochastic Variational Inference
  ☆150Updated 6 years ago
• josejimenezluna / pyGPGO
  Bayesian optimization for Python
  ☆246Updated 3 years ago
• kirthevasank / gp-parallel-ts
  Parallelised Thompson Sampling in GPs for Bayesian Optimisation
  ☆36Updated 7 years ago
• wujian16 / Cornell-MOE
  A Python library for the state-of-the-art Bayesian optimization algorithms, with the core implemented in C++.
  ☆270Updated 5 years ago
• alshedivat / keras-gp
  Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.
  ☆249Updated last year
• duvenaud / phd-thesis
  I am in [research] stepped in so far that, should I wade no more, Returning were as tedious as go o'er. -MacBeth
  ☆185Updated 11 years ago
• oxfordcontrol / Bayesian-Optimization
  Reference implementation of Optimistic Expected Improvement.
  ☆49Updated 5 years ago
• GPflow / GPflowOpt
  Bayesian Optimization using GPflow
  ☆271Updated 4 years ago
• SheffieldML / PyDeepGP
  Deep Gaussian Processes in Python
  ☆234Updated 4 years ago
• ziyuw / rembo
  Bayesian optimization in high-dimensions via random embedding.
  ☆114Updated 12 years ago
• jamesrobertlloyd / gp-structure-search
  ☆237Updated 8 years ago
• MethodsOfMachineLearning / entropy-search
  Entropy Search for Information-Efficient Global Optimization - JMLR v13
  ☆30Updated 8 years ago
• aboustati / dgplib
  Library for Deep Gaussian Processes based on GPflow
  ☆19Updated 5 years ago
• SheffieldML / deepGP
  Deep Gaussian Processes in matlab
  ☆93Updated 3 years ago
• treforevans / gp_grief
  [ICML'18] Scalable Gaussian Processes with Grid-Structured Eigenfunctions
  ☆20Updated 3 years ago
• sods / paramz
  Parameterization Framework for parameterized model creation and handling.
  ☆49Updated last month
• yaringal / HeteroscedasticDropoutUncertainty
  Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.
  ☆140Updated 9 years ago
• mauriziofilippone / deep_gp_random_features
  ☆40Updated 6 years ago
• secondmind-labs / GPflux
  Deep GPs built on top of TensorFlow/Keras and GPflow
  ☆127Updated 9 months ago
• kasparmartens / NeuralProcesses
  Neural Processes implementation for 1D regression
  ☆65Updated 6 years ago
• hughsalimbeni / DGPs_with_IWVI
  Deep Gaussian Processes with Importance-Weighted Variational Inference
  ☆39Updated 6 years ago
• tqchen / ML-SGHMC
  Experiment code for Stochastic Gradient Hamiltonian Monte Carlo
  ☆106Updated 7 years ago
• markvdw / convgp
  Convolutional Gaussian processes based on GPflow.
  ☆95Updated 7 years ago
• vlad17 / runlmc
  Structurally efficient multi-output linearly coregionalized Gaussian Processes: it's tricky, tricky, tricky, tricky, tricky.
  ☆38Updated 2 years ago
• izmailovpavel / TTGP
  ☆26Updated 7 years ago
• PredictiveIntelligenceLab / JAX-BO
  ☆30Updated 2 years ago
• j-wilson / GPflowSampling
  Code for efficiently sampling functions from GP(flow) posteriors
  ☆72Updated 4 years ago
• jameshensman / VFF
  Variational Fourier Features
  ☆86Updated 4 years ago
• maziarraissi / ParametricGP
  Parametric Gaussian Process Regression for Big Data
  ☆45Updated 5 years ago