jmikko / fair_ERM
Fair Empirical Risk Minimization (FERM)
☆37Updated 4 years ago
Alternatives and similar repositories for fair_ERM:
Users that are interested in fair_ERM are comparing it to the libraries listed below
- Implementation of Minimax Pareto Fairness framework☆21Updated 4 years ago
- General fair regression subject to demographic parity constraint. Paper appeared in ICML 2019.☆15Updated 4 years ago
- Code for "Counterfactual Fairness" (NIPS2017)☆52Updated 6 years ago
- Python code for training fair logistic regression classifiers.☆189Updated 3 years ago
- ☆22Updated 5 years ago
- Learning Adversarially Fair and Transferable Representations☆54Updated 6 years ago
- ☆37Updated last year
- ☆50Updated last year
- Distributional Shapley: A Distributional Framework for Data Valuation☆30Updated 10 months ago
- A benchmark for distribution shift in tabular data☆50Updated 8 months ago
- References for Papers at the Intersection of Causality and Fairness☆18Updated 6 years ago
- library for fair auditing and learning of classifiers with respect to rich subgroup fairness.☆32Updated 5 years ago
- This is the source code for Learning Deep Kernels for Non-Parametric Two-Sample Tests (ICML2020).☆48Updated 3 years ago
- ☆124Updated 3 years ago
- ☆42Updated 6 years ago
- Official repository for CMU Machine Learning Department's 10732: Robustness and Adaptivity in Shifting Environments☆73Updated 2 years ago
- Code and data for the experiments in "On Fairness and Calibration"☆50Updated 2 years ago
- Model Agnostic Counterfactual Explanations☆85Updated 2 years ago
- Tilted Empirical Risk Minimization (ICLR '21)☆59Updated last year
- Interpretation of Neural Network is Fragile☆36Updated 10 months ago
- ☆31Updated 3 years ago
- Codebase for "Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains Its Predictions" (to appear in AA…☆74Updated 7 years ago
- Local explanations with uncertainty 💐!☆39Updated last year
- Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning (AISTATS 2022 Oral)☆40Updated 2 years ago
- Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831☆36Updated 2 years ago
- ☆22Updated 6 years ago
- Code for Environment Inference for Invariant Learning (ICML 2021 Paper)☆50Updated 3 years ago
- PyTorch implementation of efficient algorithms for DRO with CVaR and Chi-Square uncertainty sets☆59Updated 2 years ago
- Implementation of Invariant Risk Minimization https://arxiv.org/abs/1907.02893☆85Updated 5 years ago
- Principled learning method for Wasserstein distributionally robust optimization with local perturbations (ICML 2020)☆21Updated last year