Alternatives and similar repositories for conformal_training

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

• aangelopoulos / conformal-risk
  Conformal prediction for controlling monotonic risk functions. Simple accompanying PyTorch code for conformal risk control in computer vi…
  ☆73Updated 2 years ago
• TorchUQ / torchuq
  A library for uncertainty quantification based on PyTorch
  ☆122Updated 3 years ago
• microsoft / bayesianize
  Bayesianize: A Bayesian neural network wrapper in pytorch
  ☆90Updated last year
• probabilisticai / probai-2023
  Materials of the Nordic Probabilistic AI School 2023.
  ☆91Updated 2 years ago
• aangelopoulos / rcps
  Official codebase for "Distribution-Free, Risk-Controlling Prediction Sets"
  ☆87Updated last year
• facebookresearch / nbm-spam
  Training and evaluating NBM and SPAM for interpretable machine learning.
  ☆78Updated 2 years ago
• aangelopoulos / ltt
  Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control
  ☆73Updated last year
• bremen79 / parameterfree
  Parameter-Free Optimizers for Pytorch
  ☆130Updated last year
• runame / laplace-redux
  Laplace Redux -- Effortless Bayesian Deep Learning
  ☆43Updated 6 months ago
• probabilisticai / probai-2022
  Materials of the Nordic Probabilistic AI School 2022.
  ☆181Updated 3 years ago
• bmucsanyi / untangle
  Large-scale uncertainty benchmark in deep learning.
  ☆64Updated 7 months ago
• ifiaposto / Distance_Aware_Bottleneck
  Official Implementation of the paper: "A Rate-Distorion View of Uncertainty Quantification", ICML 2024
  ☆28Updated last year
• adrianjav / heterogeneous_vaes
  Pytorch implementation of VAEs for heterogeneous likelihoods.
  ☆43Updated 3 years ago
• aangelopoulos / prediction-powered-inference
  A statistical toolkit for scientific discovery using machine learning
  ☆79Updated last year
• ml-jku / quam
  Quantification of Uncertainty with Adversarial Models
  ☆29Updated 2 years ago
• apple / ml-calibration
  relplot: Utilities for measuring calibration and plotting reliability diagrams
  ☆175Updated last month
• facebookresearch / w2ot
  Euclidean Wasserstein-2 optimal transportation
  ☆47Updated 2 years ago
• jambo6 / neuralRDEs
  Code for: "Neural Rough Differential Equations for Long Time Series", (ICML 2021)
  ☆121Updated 4 years ago
• openopt / chop
  CHOP: An optimization library based on PyTorch, with applications to adversarial examples and structured neural network training.
  ☆78Updated last year
• dobriban / Topics-In-Modern-Statistical-Learning
  Materials for STAT 991: Topics In Modern Statistical Learning (UPenn, 2022 Spring) - uncertainty quantification, conformal prediction, ca…
  ☆177Updated last year
• jjcherian / conditional-conformal
  A package for conformal prediction with conditional guarantees.
  ☆67Updated 2 months ago
• automl / TransformersCanDoBayesianInference
  Official Implementation of "Transformers Can Do Bayesian Inference", the PFN paper
  ☆244Updated last year
• hsouri / BayesianTransferLearning
  ☆110Updated 3 years ago
• normal-computing / posteriors
  Uncertainty quantification with PyTorch
  ☆378Updated 2 months ago
• wiseodd / last_layer_laplace
  Last-layer Laplace approximation code examples
  ☆83Updated 4 years ago
• microsoft / goodpoints
  A Python package for generating concise, high-quality summaries of a probability distribution
  ☆55Updated last month
• ratschlab / bnn_priors
  Code for the paper "Bayesian Neural Network Priors Revisited"
  ☆58Updated 4 years ago
• g-benton / loss-surface-simplexes
  ☆100Updated 4 years ago
• google-deepmind / neural_testbed
  ☆192Updated 6 months ago
• mkirchhof / url
  Uncertainty-aware representation learning (URL) benchmark
  ☆105Updated 9 months ago