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
  ☆13Updated 2 years ago
• secondmind-labs / bayesian_benchmarks
  A community repository for benchmarking Bayesian methods
  ☆11Updated 2 years ago
• juankuntz / ParEM
  Code for "Particle algorithms for maximum likelihood training of latent variable models" (Kuntz, Lim, Johansen, AISTATS, 2023).
  ☆9Updated 2 years ago
• joneswack / dp-rfs
  This repository contains PyTorch implementations of various random feature maps for dot product kernels.
  ☆21Updated last year
• 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
• LaurenceA / bayesfunc
  ☆15Updated 2 years ago
• fidero / gp-derivative
  Example code to speed up GP inference with gradients for high-dimensional inputs.
  ☆14Updated 4 years ago
• pmorenoz / ModularGP
  Modular Gaussian Processes
  ☆15Updated 3 years ago
• NickHunt-Smith / MCMC-diffusion
  A Metropolis-Hastings MCMC sampler accelerated via diffusion models
  ☆14Updated last year
• kimvc7 / HDL
  ☆12Updated 2 years ago
• PrincetonLIPS / AHGP
  [NeurIPS 2020] Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters (AHGP)
  ☆22Updated 4 years ago
• AaltoML / spatio-temporal-GPs
  Code for NeurIPS 2021 paper 'Spatio-Temporal Variational Gaussian Processes'
  ☆47Updated 3 years ago
• ltiao / bore
  Bayesian Optimization with Density-Ratio Estimation
  ☆24Updated 2 years ago
• hegdepashupati / gaussian-process-odes
  Implementation of the work Variational multiple shooting for Bayesian ODEs with Gaussian processes
  ☆12Updated 3 years ago
• cambridge-mlg / sgd-gp
  Public code for running Stochastic Gradient Descent on GPs.
  ☆39Updated 3 months ago
• aterenin / SparseGPAX
  An ultra-lightweight JAX implementation of sparse Gaussian processes via pathwise sampling.
  ☆22Updated 4 years ago
• AdrienCorenflos / particle_mala
  Gradient-informed particle MCMC methods
  ☆12Updated last year
• HanbaekLyu / NNetwork
  Custom graph/network/multi-weighted network class based on storing list of neighbors for each nodes (as opposed to edge list) for scalabl…
  ☆11Updated last year
• zalandoresearch / JaxRK
  RKHS feature vectors, operators, and statistical models using JAX for automatic differentiation
  ☆8Updated 4 years ago
• wjmaddox / spectralgp
  Code repo for "Function-Space Distributions over Kernels"
  ☆32Updated 4 years ago
• desi-ivanova / idad
  Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods
  ☆20Updated 3 years ago
• j-wilson / GPflowSampling
  Code for efficiently sampling functions from GP(flow) posteriors
  ☆72Updated 4 years ago
• desResLab / LINFA
  A Python library for variational inference with normalizing flow and annealing
  ☆16Updated 4 months ago
• OxfordML / bayesquad
  Library for Bayesian Quadrature
  ☆32Updated 6 years ago
• aleximmer / BNN-predictions
  Improving predictions of Bayesian neural nets via local linearization, AISTATS 2021
  ☆16Updated 2 years ago
• Sulam-Group / k-rcps
  "How to Trust Your Diffusion Models: A Convex Optimization Approach to Conformal Risk Control"
  ☆18Updated 2 months ago
• fredhallgren / nystrompca
  Efficient non-linear PCA through kernel PCA with the Nyström method
  ☆13Updated 2 years ago
• amzn / orthogonal-additive-gaussian-processes
  Light-weighted code for Orthogonal Additive Gaussian Processes
  ☆43Updated last year
• Benjamin-Walker / log-neural-cdes
  Code for "Log Neural Controlled Differential Equations: The Lie Brackets Make a Difference" (ICML 2024)
  ☆22Updated 2 months ago