steven7woo / fair_regression_reduction
General fair regression subject to demographic parity constraint. Paper appeared in ICML 2019.
☆15Updated 4 years ago
Alternatives and similar repositories for fair_regression_reduction:
Users that are interested in fair_regression_reduction are comparing it to the libraries listed below
- ☆14Updated 10 months ago
- ☆22Updated 5 years ago
- Implementation of Minimax Pareto Fairness framework☆21Updated 4 years ago
- Fair Empirical Risk Minimization (FERM)☆37Updated 4 years ago
- ☆37Updated last year
- Code for Environment Inference for Invariant Learning (ICML 2021 Paper)☆50Updated 3 years ago
- A simple PyTorch implementation of influence functions.☆84Updated 7 months ago
- Official repository for CMU Machine Learning Department's 10732: Robustness and Adaptivity in Shifting Environments☆73Updated 2 years ago
- Code for "Just Train Twice: Improving Group Robustness without Training Group Information"☆70Updated 8 months ago
- A benchmark for distribution shift in tabular data☆50Updated 8 months ago
- Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831☆36Updated 2 years ago
- Influence Analysis and Estimation - Survey, Papers, and Taxonomy☆69Updated 11 months ago
- Code for "Counterfactual Fairness" (NIPS2017)☆52Updated 6 years ago
- code release for the paper "On Completeness-aware Concept-Based Explanations in Deep Neural Networks"☆52Updated 2 years ago
- Simple data balancing baselines for worst-group-accuracy benchmarks.☆41Updated last year
- A reproduced PyTorch implementation of the Adversarially Reweighted Learning (ARL) model, originally presented in "Fairness without Demog…☆21Updated 4 years ago
- ☆43Updated 2 years ago
- An Empirical Framework for Domain Generalization In Clinical Settings☆29Updated 2 years ago
- Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning (AISTATS 2022 Oral)☆40Updated 2 years ago
- This is a PyTorch reimplementation of Influence Functions from the ICML2017 best paper: Understanding Black-box Predictions via Influence…☆16Updated 4 years ago
- Do input gradients highlight discriminative features? [NeurIPS 2021] (https://arxiv.org/abs/2102.12781)☆13Updated 2 years ago
- FairBatch: Batch Selection for Model Fairness (ICLR 2021)☆19Updated last year
- ☆38Updated 3 years ago
- ☆34Updated last year
- Official code for "In Search of Robust Measures of Generalization" (NeurIPS 2020)☆28Updated 4 years ago
- The Pitfalls of Simplicity Bias in Neural Networks [NeurIPS 2020] (http://arxiv.org/abs/2006.07710v2)☆39Updated last year
- References for Papers at the Intersection of Causality and Fairness☆18Updated 6 years ago
- Code for "Generative causal explanations of black-box classifiers"☆33Updated 4 years ago
- Certified Removal from Machine Learning Models☆63Updated 3 years ago
- Repository for the NeurIPS 2023 paper "Beyond Confidence: Reliable Models Should Also Consider Atypicality"☆12Updated 9 months ago