Alternatives and similar repositories for L2X

Users that are interested in L2X are comparing it to the libraries listed below

• BeenKim / MMD-critic
  ☆134Updated 5 years ago
• csinva / hierarchical-dnn-interpretations
  Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)
  ☆128Updated 3 years ago
• OscarcarLi / PrototypeDL
  Codebase for "Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains Its Predictions" (to appear in AA…
  ☆75Updated 7 years ago
• GDPlumb / MAPLE
  Supervised Local Modeling for Interpretability
  ☆29Updated 6 years ago
• gpleiss / equalized_odds_and_calibration
  Code and data for the experiments in "On Fairness and Calibration"
  ☆51Updated 3 years ago
• d909b / cxplain
  Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.
  ☆131Updated 4 years ago
• marcoancona / DASP
  Keras implementation for DASP: Deep Approximate Shapley Propagation (ICML 2019)
  ☆61Updated 6 years ago
• laura-rieger / deep-explanation-penalization
  Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" ht…
  ☆127Updated 4 years ago
• suinleelab / attributionpriors
  Tools for training explainable models using attribution priors.
  ☆124Updated 4 years ago
• wOOL / DNDT
  Deep Neural Decision Trees
  ☆162Updated 3 years ago
• google / TrustScore
  To Trust Or Not To Trust A Classifier. A measure of uncertainty for any trained (possibly black-box) classifier which is more effective t…
  ☆176Updated 2 years ago
• charmlab / mace
  Model Agnostic Counterfactual Explanations
  ☆87Updated 2 years ago
• equialgo / fairness-in-ml
  This repository contains the full code for the "Towards fairness in machine learning with adversarial networks" blog post.
  ☆117Updated 4 years ago
• kohpangwei / group-influence-release
  ☆50Updated 2 years ago
• dtak / rrr
  Code/figures in Right for the Right Reasons
  ☆55Updated 4 years ago
• iancovert / removal-explanations
  A lightweight implementation of removal-based explanations for ML models.
  ☆59Updated 3 years ago
• GoudetOlivier / CGNN
  Replication code for the article "Learning Functional Causal Models with Generative Neural Networks"
  ☆100Updated 6 years ago
• chihkuanyeh / Representer_Point_Selection
  code release for Representer point Selection for Explaining Deep Neural Network in NeurIPS 2018
  ☆67Updated 3 years ago
• microsoft / EDDI
  Code for paper EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
  ☆40Updated 2 years ago
• Piyushi-0 / ACE
  Code for our ICML '19 paper: Neural Network Attributions: A Causal Perspective.
  ☆51Updated 3 years ago
• SeojinBang / VIBI
  Explaining a black-box using Deep Variational Information Bottleneck Approach
  ☆46Updated 2 years ago
• blei-lab / deconfounder_tutorial
  ☆87Updated 5 years ago
• jsyoon0823 / INVASE
  Codebase for INVASE: Instance-wise Variable Selection - 2019 ICLR
  ☆62Updated 5 years ago
• marcotcr / anchor-experiments
  Experiments for AAAI anchor paper
  ☆63Updated 7 years ago
• maxwshen / iap-cidl
  Causal Inference & Deep Learning, MIT IAP 2018
  ☆89Updated 7 years ago
• chenhongge / RobustTrees
  [ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples
  ☆67Updated this week
• mtsang / neural-interaction-detection
  Detecting Statistical Interactions from Neural Network Weights
  ☆48Updated 5 years ago
• nke001 / causal_learning_unknown_interventions
  Code for "Neural causal learning from unknown interventions"
  ☆103Updated 5 years ago
• mbilalzafar / fair-classification
  Python code for training fair logistic regression classifiers.
  ☆189Updated 3 years ago
• dmadras / predict-responsibly
  ☆14Updated last year