Alternatives and similar repositories for ECRT:

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

• mfederici / dsit
  Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"
  ☆25Updated 3 years ago
• AMLab-Amsterdam / DataAugmentationInterventions
  ☆27Updated 4 years ago
• GT-RIPL / Geometric-Sensitivity-Decomposition
  ☆18Updated 3 years ago
• alexrame / diwa
  DiWA: Diverse Weight Averaging for Out-of-Distribution Generalization
  ☆29Updated 2 years ago
• mostafaalishahi / eICU_Benchmark_updated
  An updated version of eICU Benchmark with an updated problem definition on LoS and Decompensation tasks
  ☆11Updated 3 years ago
• kakaobrain / irm-empirical-study
  An Empirical Study of Invariant Risk Minimization
  ☆27Updated 4 years ago
• yaohungt / Self_Supervised_Learning_Multiview
  ☆42Updated 4 years ago
• inspire-group / robustness-via-transport
  ☆12Updated 5 years ago
• mertyg / beyond-confidence-atypicality
  Repository for the NeurIPS 2023 paper "Beyond Confidence: Reliable Models Should Also Consider Atypicality"
  ☆12Updated 11 months ago
• HazyResearch / mandoline
  (ICML 2021) Mandoline: Model Evaluation under Distribution Shift
  ☆31Updated 3 years ago
• ae-foster / invclr
  Improving Transformation Invariance in Contrastive Representation Learning
  ☆13Updated 4 years ago
• mrojascarulla / causal_transfer_learning
  ☆32Updated 6 years ago
• Kennethborup / self_distillation
  Self-Distillation with weighted ground-truth targets; ResNet and Kernel Ridge Regression
  ☆18Updated 3 years ago
• ahujak / IB-IRM
  ☆30Updated 3 years ago
• stanislavfort / adversaries_to_OOD_detection
  ☆12Updated last year
• lmz123321 / which_invariance
  Official implementation for ICML23 paper: Which Invariance Should We Transfer? A Causal Minimax Learning Approach
  ☆11Updated 10 months ago
• JonathanWenger / pycalib
  Non-Parametric Calibration for Classification (AISTATS 2020)
  ☆19Updated 3 years ago
• mgong2 / DA_Infer
  Domain Adaptation as a Problem of Inference on Graphical Models
  ☆29Updated 4 years ago
• alextamkin / active-learning-pretrained-models
  Active Learning Helps Pretrained Models Learn the Intended Task (https://arxiv.org/abs/2204.08491) by Alex Tamkin, Dat Nguyen, Salil Desh…
  ☆11Updated 2 years ago
• thudzj / NEigenmaps
  ☆12Updated last year
• jlko / active-testing
  Active and Sample-Efficient Model Evaluation
  ☆24Updated 4 years ago
• bhanML / SIGUA
  ICML'20: SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
  ☆15Updated 4 years ago
• takeshi-teshima / few-shot-domain-adaptation-by-causal-mechanism-transfer
  Implementation of Few-shot Domain Adaptation by Causal Mechanism Transfer (ICML 2020)
  ☆42Updated 2 years ago
• VirtuosoResearch / Regularized-Self-Labeling
  A regularized self-labeling approach to improve the generalization and robustness of fine-tuned models
  ☆28Updated 2 years ago
• divamgupta / deep-clustering-kingdra
  Official implementation of ICLR 2020 paper Unsupervised Clustering using Pseudo-semi-supervised Learning
  ☆47Updated 4 years ago
• anndvision / quince
  Code for Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding
  ☆22Updated 2 years ago
• YilunZhou / optimal-active-learning
  Code repository for the AISTATS 2021 paper "Towards Understanding the Optimal Behaviors of Deep Active Learning Algorithms"
  ☆15Updated 4 years ago
• Lifelong-ML / LASEM
  Code for the ICML 2021 paper "Sharing Less is More: Lifelong Learning in Deep Networks with Selective Layer Transfer"
  ☆11Updated 3 years ago
• Newbeeer / orthogonal_classifier
  Code for ICLR 2022 Paper, "Controlling Directions Orthogonal to a Classifier"
  ☆35Updated last year
• IandRover / MAML_noisy_contrasive_learner
  ☆17Updated 2 years ago