Alternatives and similar repositories for Max-value-Entropy-Search

Users that are interested in Max-value-Entropy-Search are comparing it to the libraries listed below

• zi-w / Structural-Kernel-Learning-for-HDBBO
  Batched High-dimensional Bayesian Optimization via Structural Kernel Learning
  ☆15Updated 7 years ago
• kirthevasank / mf-gp-ucb
  Multi-fidelity Gaussian Process Bandit Optimisation
  ☆39Updated 9 years ago
• ebonilla / mtgp
  Multi-task Gaussian Process
  ☆45Updated 11 years ago
• MethodsOfMachineLearning / entropy-search
  Entropy Search for Information-Efficient Global Optimization - JMLR v13
  ☆30Updated 8 years ago
• SheffieldML / multigp
  Multiple output Gaussian processes in MATLAB including the latent force model.
  ☆52Updated 10 years ago
• thangbui / streaming_sparse_gp
  Streaming sparse Gaussian process approximations
  ☆69Updated 3 years ago
• RaulAstudillo06 / BOCF
  A framework for Bayesian optimization of composite functions.
  ☆15Updated 3 years ago
• ntienvu / KnownOptimum_BO
  Release code for ICML2020 Knowing The What But Not The Where in Bayesian Optimization
  ☆15Updated 2 years ago
• RogerFrigola / VGPSSM
  Variational Gaussian Process State-Space Models
  ☆25Updated 10 years ago
• kirthevasank / add-gp-bandits
  Additive Gaussian Process Bandits - version 1.0
  ☆27Updated 9 years ago
• thangbui / sparseGP_powerEP
  Unifying sparse approximations for Gaussian process regression and classification, using Power EP
  ☆22Updated 9 years ago
• SheffieldML / deepGP
  Deep Gaussian Processes in matlab
  ☆93Updated 4 years ago
• ziyuw / rembo
  Bayesian optimization in high-dimensions via random embedding.
  ☆116Updated 12 years ago
• pmorenoz / HetMOGP
  Heterogeneous Multi-output Gaussian Processes
  ☆54Updated 5 years ago
• UCL-SML / Doubly-Stochastic-DGP
  Deep Gaussian Processes with Doubly Stochastic Variational Inference
  ☆151Updated 6 years ago
• SheffieldML / PyDeepGP
  Deep Gaussian Processes in Python
  ☆236Updated 4 years 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 8 years ago
• kirthevasank / gp-parallel-ts
  Parallelised Thompson Sampling in GPs for Bayesian Optimisation
  ☆36Updated 8 years ago
• baptistar / BOCS
  Bayesian Optimization of Combinatorial Structures
  ☆102Updated 6 years ago
• aminnayebi / HesBO
  The High-dimensional BayesOpt algorithms from "A Framework for Bayesian Optimization in Embedded Subspaces
  ☆43Updated 6 years ago
• daizhongxiang / sto-bnts
  Official implementation for the paper "Sample-Then-Optimize Batch Neural Thompson Sampling", published at NeurIPS 2022.
  ☆10Updated 3 years ago
• automl / pybnn
  Bayesian neural network package
  ☆155Updated 4 years ago
• belakaria / MESMO
  Python implementation of the Max-value Entropy Search for Multi-Objective Bayesian Optimization method
  ☆27Updated 6 months ago
• wujian16 / TwoStep-BayesOpt
  NeurIPS 2019 Paper
  ☆11Updated 6 years ago
• cagatayyildiz / npode
  Learning unknown ODE models with Gaussian processes
  ☆27Updated 7 years ago
• gianlucadetommaso / Stein-variational-samplers
  Python and MATLAB code for Stein Variational sampling methods
  ☆27Updated 6 years ago
• zi-w / Ensemble-Bayesian-Optimization
  Code repository for Ensemble Bayesian Optimization
  ☆57Updated 6 years ago
• DartML / Stein-Variational-Gradient-Descent
  code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"
  ☆101Updated 6 years ago
• ae-foster / pyro
  Deep universal probabilistic programming with Python and PyTorch
  ☆12Updated 5 years ago
• OxfordML / GPz
  GPz 2.0: Heteroscedastic Gaussian processes for uncertain and incomplete data
  ☆49Updated 4 years ago