Alternatives and similar repositories for mixed_KSG

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

• ivaleraM / GLFM
  General Latent Feature Modeling for Heterogeneous data
  ☆50Updated last year
• Jianbo-Lab / L2X
  ☆125Updated 4 years ago
• jameshensman / VFF
  Variational Fourier Features
  ☆87Updated 4 years ago
• mauriziofilippone / deep_gp_random_features
  ☆40Updated 6 years ago
• GoudetOlivier / CGNN
  Replication code for the article "Learning Functional Causal Models with Generative Neural Networks"
  ☆100Updated 6 years ago
• ericsuh / dirichlet
  Dirichlet MLE python library
  ☆117Updated 5 months ago
• PGM-Lab / InferPy
  InferPy: Deep Probabilistic Modeling with Tensorflow Made Easy
  ☆148Updated last year
• kasparmartens / NeuralProcesses
  Neural Processes implementation for 1D regression
  ☆64Updated 6 years ago
• trevorcampbell / bayesian-coresets
  Automated Scalable Bayesian Inference
  ☆131Updated 4 years ago
• UCL-SML / Doubly-Stochastic-DGP
  Deep Gaussian Processes with Doubly Stochastic Variational Inference
  ☆151Updated 6 years ago
• lopezpaz / causation_learning_theory
  Code for "Towards a learning theory of cause-effect inference" (ICML 2015).
  ☆30Updated 5 years ago
• blei-lab / variational-smc
  Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)
  ☆66Updated 6 years ago
• hrayrhar / T-CorEx
  Implementation of linear CorEx and temporal CorEx.
  ☆36Updated 4 years ago
• yaringal / HeteroscedasticDropoutUncertainty
  Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.
  ☆141Updated 9 years ago
• wittawatj / interpretable-test
  NeurIPS 2016. Linear-time interpretable nonparametric two-sample test.
  ☆64Updated 7 years ago
• BeenKim / MMD-critic
  ☆135Updated 6 years ago
• rsheth80 / pmf-automl
  Data and code related to the paper "Probabilistic matrix factorization for automated machine learning", NIPS, 2018.
  ☆40Updated 4 years ago
• BigBayes / SGRLD
  Stochastic Gradient Riemannian Langevin Dynamics
  ☆34Updated 10 years ago
• openopt / chop
  CHOP: An optimization library based on PyTorch, with applications to adversarial examples and structured neural network training.
  ☆78Updated last year
• wangtongada / BOA
  Bayesian or-of-and
  ☆36Updated 3 years ago
• namkoong-lab / robustopt
  ☆43Updated 7 years ago
• ahmedmalaa / Symbolic-Metamodeling
  Codebase for "Demystifying Black-box Models with Symbolic Metamodels", NeurIPS 2019.
  ☆52Updated 6 years ago
• MatthewWilletts / GM-DGM
  A deep generative model of semi-unsupervised learning
  ☆15Updated 7 years ago
• karlnapf / ds3_kernel_testing
  Material for the practical of the DS3 course on "Representing and comparing probabilities with kernels"
  ☆26Updated 6 years ago
• RobRomijnders / weight_uncertainty
  Implementing Bayes by Backprop
  ☆184Updated 6 years ago
• clinicalml / dmm
  Deep Markov Models
  ☆132Updated 6 years ago
• gpleiss / equalized_odds_and_calibration
  Code and data for the experiments in "On Fairness and Calibration"
  ☆51Updated 3 years ago
• chenhongge / RobustTrees
  [ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples
  ☆69Updated 6 months ago
• MFreidank / pysgmcmc
  Bayesian Deep Learning with Stochastic Gradient MCMC Methods
  ☆38Updated 4 years ago
• akshaykgupta / SG_MCMC
  Implementation of Stochastic Gradient MCMC algorithms
  ☆41Updated 9 years ago