Alternatives and similar repositories for bayesian_optimization

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

• 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
  ☆188Updated 11 years ago
• jmetzen / gp_extras
  Additional kernels that can be used with scikit-learn's Gaussian Process module
  ☆82Updated last year
• wujian16 / Cornell-MOE
  A Python library for the state-of-the-art Bayesian optimization algorithms, with the core implemented in C++.
  ☆272Updated 5 years ago
• SheffieldML / PyDeepGP
  Deep Gaussian Processes in Python
  ☆236Updated 4 years ago
• ziyuw / rembo
  Bayesian optimization in high-dimensions via random embedding.
  ☆115Updated 12 years ago
• josejimenezluna / pyGPGO
  Bayesian optimization for Python
  ☆246Updated 3 years ago
• UCL-SML / Doubly-Stochastic-DGP
  Deep Gaussian Processes with Doubly Stochastic Variational Inference
  ☆151Updated 6 years ago
• kirthevasank / gp-parallel-ts
  Parallelised Thompson Sampling in GPs for Bayesian Optimisation
  ☆36Updated 8 years ago
• oxfordcontrol / Bayesian-Optimization
  Reference implementation of Optimistic Expected Improvement.
  ☆50Updated 5 years ago
• alshedivat / keras-gp
  Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.
  ☆250Updated last year
• SheffieldML / deepGP
  Deep Gaussian Processes in matlab
  ☆92Updated 4 years ago
• GPflow / GPflowOpt
  Bayesian Optimization using GPflow
  ☆272Updated 4 years ago
• jamesrobertlloyd / gp-structure-search
  ☆241Updated 8 years ago
• dfm / gp
  A tutorial about Gaussian process regression
  ☆191Updated 5 years ago
• LiuHaiTao01 / SVSHGP
  Stochastic variational heteroscedastic Gaussian process
  ☆15Updated 6 years ago
• tqchen / ML-SGHMC
  Experiment code for Stochastic Gradient Hamiltonian Monte Carlo
  ☆110Updated 7 years ago
• kasparmartens / NeuralProcesses
  Neural Processes implementation for 1D regression
  ☆64Updated 6 years ago
• automl / pybnn
  Bayesian neural network package
  ☆152Updated 4 years ago
• mauriziofilippone / deep_gp_random_features
  ☆40Updated 6 years ago
• jamesrobertlloyd / gpss-research
  Kernel structure discovery research code - likely to be unstable
  ☆193Updated 10 years ago
• sods / paramz
  Parameterization Framework for parameterized model creation and handling.
  ☆49Updated 4 months ago
• maka89 / Deep-Kernel-GP
  Deep Kernel Learning. Gaussian Process Regression where the input is a neural network mapping of x that maximizes the marginal likelihood
  ☆95Updated 7 years ago
• secondmind-labs / GPflux
  Deep GPs built on top of TensorFlow/Keras and GPflow
  ☆127Updated last year
• maziarraissi / ParametricGP
  Parametric Gaussian Process Regression for Big Data
  ☆45Updated 5 years ago
• marionmari / pyGPs
  pyGPs is a library containing an object-oriented python implementation for Gaussian Process (GP) regression and classification.
  ☆216Updated 6 years ago
• hughsalimbeni / DGPs_with_IWVI
  Deep Gaussian Processes with Importance-Weighted Variational Inference
  ☆39Updated 6 years ago
• j-wilson / GPflowSampling
  Code for efficiently sampling functions from GP(flow) posteriors
  ☆73Updated 5 years ago
• MethodsOfMachineLearning / entropy-search
  Entropy Search for Information-Efficient Global Optimization - JMLR v13
  ☆30Updated 8 years ago
• anassinator / gp
  Differentiable Gaussian Process implementation for PyTorch
  ☆22Updated 7 years ago
• markvdw / convgp
  Convolutional Gaussian processes based on GPflow.
  ☆95Updated 8 years ago