Alternatives and similar repositories for nips2017

Users that are interested in nips2017 are comparing it to the libraries listed below

• gpleiss / equalized_odds_and_calibration
  Code and data for the experiments in "On Fairness and Calibration"
  ☆51Updated 3 years ago
• kohpangwei / data-poisoning-release
  ☆32Updated 7 years ago
• ermongroup / lag-fairness
  ☆22Updated 6 years ago
• zjelveh / learning-fair-representations
  Python numba implementation of Zemel et al. 2013
  ☆26Updated 4 years ago
• nikikilbertus / blind-justice
  Blind Justice Code for the paper "Blind Justice: Fairness with Encrypted Sensitive Attributes", ICML 2018
  ☆14Updated 6 years ago
• jtcho / FairMachineLearning
  Implementation of provably Rawlsian fair ML algorithms for contextual bandits.
  ☆14Updated 8 years ago
• peterbhase / InterpretableNLP-ACL2020
  Code for "Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?"
  ☆45Updated last year
• amiratag / InterpretationFragility
  Interpretation of Neural Network is Fragile
  ☆36Updated last year
• yanaiela / demog-text-removal
  ☆51Updated 6 years ago
• VectorInstitute / laftr
  Learning Adversarially Fair and Transferable Representations
  ☆54Updated 6 years ago
• max-andr / provably-robust-boosting
  Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks [NeurIPS 2019]
  ☆50Updated 5 years ago
• lydiatliu / delayedimpact
  Code for reproducing results in Delayed Impact of Fair Machine Learning (Liu et al 2018)
  ☆14Updated 2 years ago
• kohpangwei / group-influence-release
  ☆50Updated 2 years ago
• algofairness / fairness-comparison
  Comparing fairness-aware machine learning techniques.
  ☆159Updated 2 years ago
• choprashweta / Adversarial-Debiasing
  Implementation of Adversarial Debiasing in PyTorch to address Gender Bias
  ☆31Updated 4 years ago
• SeojinBang / VIBI
  Explaining a black-box using Deep Variational Information Bottleneck Approach
  ☆46Updated 2 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 4 years ago
• sisinflab / amlrecsys-tutorial
  Tutorial by Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia and Felice Antonio Merra about Adversarial Machine Learning in Recommende…
  ☆25Updated 4 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
• GitHubLuCheng / LTEE
  Implementation of paper Long-Term Effect Estimation with Surrogate Representation
  ☆12Updated 4 years ago
• xuanqing94 / BayesianDefense
  Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network
  ☆62Updated 5 years ago
• vikramnitin9 / adversarial-learning-literature
  ☆37Updated 5 years ago
• blei-lab / deconfounder_tutorial
  ☆87Updated 5 years ago
• uclanlp / reducingbias
  ☆83Updated 4 years ago
• chenhongge / RobustTrees
  [ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples
  ☆67Updated 2 years ago
• max-andr / provable-robustness-max-linear-regions
  Provable Robustness of ReLU networks via Maximization of Linear Regions [AISTATS 2019]
  ☆32Updated 4 years ago
• csong27 / collision-bert
  ☆27Updated 4 years ago
• eth-sri / lcifr
  Learning Certified Individually Fair Representations
  ☆24Updated 4 years ago
• lethaiq / GRACE_KDD20
  GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model’s Prediction. Thai Le, Suhang Wang, Dongwon …
  ☆21Updated 4 years ago
• BeenKim / MMD-critic
  ☆134Updated 5 years ago