natalialmg / MMPFLinks
Implementation of Minimax Pareto Fairness framework
☆22Updated 5 years ago
Alternatives and similar repositories for MMPF
Users that are interested in MMPF are comparing it to the libraries listed below
Sorting:
- ☆12Updated 4 years ago
- General fair regression subject to demographic parity constraint. Paper appeared in ICML 2019.☆16Updated 5 years ago
- Certified Removal from Machine Learning Models☆69Updated 4 years ago
- Tilted Empirical Risk Minimization (ICLR '21)☆60Updated 2 years ago
- Learning rate adaptation for differentially private stochastic gradient descent☆17Updated 4 years ago
- ☆22Updated 6 years ago
- Federated posterior averaging implemented in JAX☆53Updated 2 years ago
- FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods.☆31Updated last year
- ☆21Updated 3 years ago
- R-GAP: Recursive Gradient Attack on Privacy [Accepted at ICLR 2021]☆37Updated 2 years ago
- [NeurIPS 2020] code for "Boundary thickness and robustness in learning models"☆20Updated 5 years ago
- ☆22Updated 6 years ago
- A reproduced PyTorch implementation of the Adversarially Reweighted Learning (ARL) model, originally presented in "Fairness without Demog…☆20Updated 4 years ago
- ☆31Updated 4 years ago
- FairBatch: Batch Selection for Model Fairness (ICLR 2021)☆19Updated 2 years ago
- ☆15Updated 2 years ago
- Fair Empirical Risk Minimization (FERM)☆37Updated 5 years ago
- ☆27Updated 3 years ago
- Salvaging Federated Learning by Local Adaptation☆56Updated last year
- ☆50Updated 2 years ago
- FedDANE: A Federated Newton-Type Method (Asilomar Conference on Signals, Systems, and Computers ‘19)☆26Updated 2 years ago
- ☆13Updated 3 years ago
- ☆37Updated 2 years ago
- ☆32Updated last year
- Code repository is for "Federated Composite Optimization", to appear in ICML 2021☆12Updated 3 years ago
- Papers and online resources related to machine learning fairness☆75Updated 2 years ago
- ☆51Updated last year
- Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.☆175Updated 2 years ago
- Data-efficient Training of Machine Learning Models☆69Updated 5 years ago
- Learning Adversarially Fair and Transferable Representations☆56Updated 7 years ago