Alternatives and similar repositories for Symbolic-Metamodeling

Users that are interested in Symbolic-Metamodeling are comparing it to the libraries listed below

• TorchUQ / torchuq
  A library for uncertainty quantification based on PyTorch
  ☆121Updated 3 years ago
• JonathanCrabbe / Symbolic-Pursuit
  Github for the NIPS 2020 paper "Learning outside the black-box: at the pursuit of interpretable models"
  ☆15Updated 2 years ago
• OxfordML / bayesquad
  Library for Bayesian Quadrature
  ☆32Updated 6 years ago
• PredictiveIntelligenceLab / JAX-BO
  ☆30Updated 2 years ago
• JeremiasKnoblauch / GVIPublic
  Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)
  ☆20Updated 6 years ago
• rgiordan / vittles
  "Variational inference tools to leverage estimator sensitivity."
  ☆16Updated 2 years ago
• aterenin / SparseGPAX
  An ultra-lightweight JAX implementation of sparse Gaussian processes via pathwise sampling.
  ☆22Updated 4 years ago
• ratschlab / bnn_priors
  Code for the paper "Bayesian Neural Network Priors Revisited"
  ☆58Updated 3 years ago
• ivaleraM / GLFM
  General Latent Feature Modeling for Heterogeneous data
  ☆49Updated last year
• samuelstanton / conformal-bayesopt
  Bayesian optimization with conformal coverage guarantees
  ☆28Updated 2 years ago
• vr308 / Generalised-Gaussian-Processes
  Fully Bayesian Inference in GPs - Gaussian and Generic Likelihoods
  ☆22Updated last year
• joneswack / dp-rfs
  This repository contains PyTorch implementations of various random feature maps for dot product kernels.
  ☆21Updated 10 months ago
• lawrennd / old_talks
  Talks from Neil Lawrence
  ☆54Updated last year
• ksanjeevan / mapper-tda
  Mapper implementation (Topological Data Analysis) in Python
  ☆65Updated 6 years ago
• OATML / ucate
  Uncertainty in Conditional Average Treatment Effect Estimation
  ☆32Updated 4 years ago
• MFreidank / pysgmcmc
  Bayesian Deep Learning with Stochastic Gradient MCMC Methods
  ☆39Updated 3 years ago
• yannadani / cbed
  Official implementation of the paper "Interventions, Where and How? Experimental Design for Causal Models at Scale", NeurIPS 2022.
  ☆20Updated 2 years ago
• yuachen / CausalDA
  Code to reproduce the numerical experiments in the paper Domain adaptation under structural causal models by Yuansi Chen and Peter Bühlma…
  ☆18Updated 3 years ago
• ae-foster / dad
  Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design
  ☆32Updated 3 years ago
• wjmaddox / drbayes
  Code Repo for "Subspace Inference for Bayesian Deep Learning"
  ☆82Updated 11 months ago
• adrianjav / heterogeneous_vaes
  Pytorch implementation of VAEs for heterogeneous likelihoods.
  ☆42Updated 2 years ago
• juangamella / causal-chamber
  Public dataset repository for the Causal Chamber Project
  ☆40Updated last month
• jejjohnson / research_journal
  My Research Journal covering various topics that interest me. They're mostly scattered notes and resources.
  ☆33Updated 2 years ago
• msesia / chr
  Conformal Histogram Regression: efficient conformity scores for non-parametric regression problems
  ☆22Updated 3 years ago
• karalets / TyXe
  Library for Bayesian Neural Networks in PyTorch (first version as published in ProbProg2020)
  ☆42Updated 3 years ago
• StannisZhou / mixed_hmc
  ☆12Updated 3 years ago
• CalculatedContent / ImplicitSelfRegularization
  Repo to accompany paper "Implicit Self-Regularization in Deep Neural Networks..."
  ☆44Updated 6 years ago
• duvenaud / additive-gps
  Source for experiments in the Additive Gaussian process paper, as well as extensions relating to dropout.
  ☆22Updated 11 years ago
• PrincetonLIPS / AHGP
  [NeurIPS 2020] Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters (AHGP)
  ☆21Updated 4 years ago
• gwgundersen / rflvm
  Random feature latent variable models in Python
  ☆22Updated last year