Alternatives and similar repositories for stein_thinning

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

• frgsimpson / kitt
  Kernel Identification Through Transformers
  ☆14Updated 2 years ago
• pmorenoz / ModularGP
  Modular Gaussian Processes
  ☆15Updated 3 years ago
• secondmind-labs / bayesian_benchmarks
  A community repository for benchmarking Bayesian methods
  ☆11Updated 2 years ago
• fidero / gp-derivative
  Example code to speed up GP inference with gradients for high-dimensional inputs.
  ☆14Updated 4 years ago
• MLO-lab / Uncertainty_Estimates_via_BVD
  The official source code to: Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition (AISTATS'23)
  ☆11Updated 2 years ago
• joneswack / dp-rfs
  This repository contains PyTorch implementations of various random feature maps for dot product kernels.
  ☆22Updated last year
• aleximmer / BNN-predictions
  Improving predictions of Bayesian neural nets via local linearization, AISTATS 2021
  ☆16Updated 2 years ago
• secondmind-labs / markovflow
  A Tensorflow based library for Time Series Modelling with Gaussian Processes
  ☆32Updated last year
• runame / laplace-refinement
  Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks
  ☆10Updated 2 years ago
• Sulam-Group / k-rcps
  "How to Trust Your Diffusion Models: A Convex Optimization Approach to Conformal Risk Control"
  ☆18Updated 3 months ago
• kimvc7 / HDL
  ☆12Updated 2 years ago
• LaurenceA / bayesfunc
  ☆15Updated 3 years ago
• NickHunt-Smith / MCMC-diffusion
  A Metropolis-Hastings MCMC sampler accelerated via diffusion models
  ☆14Updated last year
• amzn / orthogonal-additive-gaussian-processes
  Light-weighted code for Orthogonal Additive Gaussian Processes
  ☆43Updated last year
• OxfordML / bayesquad
  Library for Bayesian Quadrature
  ☆32Updated 6 years ago
• federicobergamin / riemannian-laplace-approximation
  Repository for the paper "Riemannian Laplace approximations for Bayesian neural networks"
  ☆11Updated last year
• htlambley / functional_autoencoders
  Autoencoders in Function Space: Resolution-invariant autoencoders for functional data based on the functional variational autoencoder (FV…
  ☆22Updated this week
• MichaelArbel / MMD-gradient-flow
  ☆12Updated last year
• Benjamin-Walker / log-neural-cdes
  Code for "Log Neural Controlled Differential Equations: The Lie Brackets Make a Difference" (ICML 2024)
  ☆23Updated 3 months ago
• j-wilson / GPflowSampling
  Code for efficiently sampling functions from GP(flow) posteriors
  ☆73Updated 4 years ago
• PrincetonLIPS / AHGP
  [NeurIPS 2020] Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters (AHGP)
  ☆22Updated 4 years ago
• dngfra / repulsive_ensembles
  Repo for our paper "Repulsive deep ensembles are Bayesian"
  ☆19Updated 3 years ago
• desResLab / LINFA
  A Python library for variational inference with normalizing flow and annealing
  ☆16Updated 6 months ago
• akuhren / selective_gp
  Code accompanying the paper "Probabilistic Selection of Inducing Points in Sparse Gaussian Processes".
  ☆25Updated 2 years ago
• tranbahien / you-need-a-good-prior
  All You Need is a Good Functional Prior for Bayesian Deep Learning (JMLR 2022)
  ☆20Updated 3 years ago
• cambridge-mlg / sgd-gp
  Public code for running Stochastic Gradient Descent on GPs.
  ☆39Updated 4 months ago
• AdrienCorenflos / particle_mala
  Gradient-informed particle MCMC methods
  ☆12Updated last year
• markvdw / RobustGP
  Robust initialisation of inducing points in sparse variational GP regression models.
  ☆33Updated 2 years ago
• MachineLearningBCAM / MRCpy
  A Library for Minimax Risk Classifiers
  ☆31Updated last year
• AaltoML / scalable-inference-in-sdes
  Methods and experiments for assumed density SDE approximations
  ☆12Updated 3 years ago