Alternatives and similar repositories for InterpretableActiveLearning

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

• djsutherland / igms
  Implicit generative models and related stuff based on the MMD, in PyTorch
  ☆16Updated 4 years ago
• dmelis / robust_interpret
  Tools for robustness evaluation in interpretability methods
  ☆10Updated 3 years ago
• yaringal / acquisition_example
  Train a simple convnet on the MNIST dataset and evaluate the BALD acquisition function
  ☆16Updated 7 years ago
• joelouismarino / amortized-variational-filtering
  PyTorch implementation of AVF
  ☆45Updated 4 years ago
• ermongroup / BiasAndGeneralization
  ☆26Updated 6 years ago
• frkl / active-learning
  Active learning for deep learning
  ☆19Updated 6 years ago
• mfederici / dsit
  Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"
  ☆25Updated 3 years ago
• CausalML / MultipleLoggers
  Code for the paper "Optimal Off-Policy Evaluation from Multiple Logging Policies"
  ☆15Updated 3 years ago
• Zhenyu-LIAO / RMT4ELM
  A Random Matrix Approach to Extreme Learning Machine
  ☆14Updated 7 years ago
• vveitch / causal-embeddings
  Code for "Using Embeddings to Correct for Unobserved Confounding"
  ☆10Updated 5 years ago
• HazyResearch / model-patching
  Model Patching: Closing the Subgroup Performance Gap with Data Augmentation
  ☆42Updated 4 years ago
• kakaobrain / irm-empirical-study
  An Empirical Study of Invariant Risk Minimization
  ☆27Updated 4 years ago
• dmadras / predict-responsibly
  ☆14Updated last year
• peterbhase / InterpretableNLP-ACL2020
  Code for "Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?"
  ☆46Updated last year
• ermongroup / bgm
  Code for "Boosted Generative Models", AAAI 2018.
  ☆20Updated 7 years ago
• YujiaBao / Predict-then-Interpolate
  "Predict, then Interpolate: A Simple Algorithm to Learn Stable Classifiers" ICML 2021
  ☆18Updated 3 years ago
• fair-preprocessing / nips2017
  ☆26Updated 7 years ago
• ruthcfong / net2vec
  Code for Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks
  ☆30Updated 7 years ago
• GuillaumeStaermanML / FIF
  Source code for the ACML 2019 paper "Functional Isolation Forest".
  ☆21Updated 2 years ago
• RobRomijnders / bandit
  Implementation of Counterfactual risk minimization
  ☆26Updated 8 years ago
• HazyResearch / mandoline
  (ICML 2021) Mandoline: Model Evaluation under Distribution Shift
  ☆31Updated 3 years ago
• a-lucic / focus
  ☆16Updated 3 years ago
• SinaMohseni / ML-Interpretability-Evaluation-Benchmark
  This is a benchmark to evaluate machine learning local explanaitons quality generated from any explainer for text and image data
  ☆30Updated 3 years ago
• m2dsupsdlclass / azure-deployment
  Deployment instructions to get a GPU VM for the Deep Learning class
  ☆17Updated 6 years ago
• jesmith14 / REAL-ML
  The Recognizing, Exploring, and Articulating Limitations in Machine Learning research tool (REAL ML) is a set of guided activities to hel…
  ☆51Updated 3 years ago
• mmrl / disent-and-gen
  Code from the article: "The Role of Disentanglement in Generalisation" (ICLR, 2021).
  ☆22Updated 2 years ago
• goodfeli / forgetting
  Repository of code for the experiments for the ICLR submission "An Empirical Investigation of Catastrophic Forgetting in Gradient-Based N…
  ☆68Updated 11 years ago
• bartwojcik / lossgrad
  Implementation of the LOSSGRAD optimization algorithm
  ☆15Updated 6 years ago
• jtcho / FairMachineLearning
  Implementation of provably Rawlsian fair ML algorithms for contextual bandits.
  ☆14Updated 8 years ago
• csinva / disentangled-attribution-curves
  Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"
  ☆28Updated 4 years ago