Alternatives and similar repositories for DMGP

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

• ahmedmalaa / discriminative-jackknife
  Codebase for "Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions", ICML 2020.
  ☆8Updated 4 years ago
• YoungseogChung / calibrated-quantile-uq
  Training quantile models
  ☆42Updated 6 months ago
• zzzace2000 / GAMs
  Multiple Generalized Additive Models implemented in Python (EBM, XGB, Spline, FLAM). Code for our KDD 2021 paper "How Interpretable and T…
  ☆12Updated 3 years ago
• seedatnabeel / TE-CDE
  Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations (ICML 2022)
  ☆27Updated 2 years ago
• msesia / chr
  Conformal Histogram Regression: efficient conformity scores for non-parametric regression problems
  ☆22Updated 3 years ago
• msesia / arc
  Adaptive and Reliable Classification: efficient conformity scores for multi-class classification problems
  ☆31Updated 2 years ago
• larslorch / avici
  Amortized Inference for Causal Structure Learning, NeurIPS 2022
  ☆64Updated 3 months ago
• zzzace2000 / nodegam
  Code for "NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning"
  ☆46Updated 2 years ago
• sjblim / rmsn_nips_2018
  ☆39Updated 6 years ago
• piomonti / carefl
  Code for "Causal autoregressive flows" - AISTATS, 2021
  ☆45Updated 4 years ago
• timrudner / S-FSVI
  Code for the paper 'Continual Learning via Sequential Function-Space Variational Inference'
  ☆24Updated 2 years ago
• kamilest / conformal-rnn
  Implementation for Stankevičiūtė et al. "Conformal time-series forecasting", NeurIPS 2021.
  ☆74Updated 6 months ago
• pamattei / miwae
  Implementation of the MIWAE method for deep generative modelling of incomplete data sets.
  ☆41Updated last year
• vistalab-technion / vqr
  Vector Quantile Regression
  ☆19Updated 2 months ago
• dtak / ocbnn-public
  General purpose library for BNNs, and implementation of OC-BNNs in our 2020 NeurIPS paper.
  ☆38Updated 2 years ago
• mrojascarulla / causal_transfer_learning
  ☆32Updated 6 years ago
• adrianjav / heterogeneous_vaes
  Pytorch implementation of VAEs for heterogeneous likelihoods.
  ☆42Updated 2 years ago
• diviyank / SAM
  Code for the Structural Agnostic Model (https://arxiv.org/abs/1803.04929)
  ☆52Updated 4 years ago
• diningphil / graph-mixture-density-networks
  Official Repository of "Graph Mixture Density Networks" (ICML 2021)
  ☆26Updated 2 years ago
• OATML / ucate
  Uncertainty in Conditional Average Treatment Effect Estimation
  ☆32Updated 4 years ago
• runame / laplace-redux
  Laplace Redux -- Effortless Bayesian Deep Learning
  ☆42Updated 2 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
• juangamella / icp
  Python implementation of the Invariant Causal Prediction (ICP) algorithm, from the 2015 paper "Causal inference using invariant predictio…
  ☆22Updated last year
• jambo6 / online-neural-cdes
  Code for: "Neural Controlled Differential Equations for Online Prediction Tasks"
  ☆38Updated 2 years ago
• clarafy / conformal-for-design
  ☆30Updated 2 years ago
• mingzhang-yin / CoCo
  An optimization-based algorithm to accurately estimate the causal effects and robustly predict under distribution shifts. It leverages th…
  ☆14Updated 10 months ago
• sanatonek / time_series_explainability
  ☆61Updated 4 years ago
• ElementAI / causal_discovery_toolbox
  Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.
  ☆30Updated 5 years ago
• ermongroup / BCD-Nets
  Code for `BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery`, Neurips 2021
  ☆26Updated 3 years ago
• ykwon0407 / wdro_local_perturbation
  Principled learning method for Wasserstein distributionally robust optimization with local perturbations (ICML 2020)
  ☆21Updated 2 years ago