trustworthy-machine-learning / trustworthy-machine-learning.github.io
A School for All Seasons on Trustworthy Machine Learning
☆11Updated 3 years ago
Alternatives and similar repositories for trustworthy-machine-learning.github.io:
Users that are interested in trustworthy-machine-learning.github.io are comparing it to the libraries listed below
- Algorithms for Privacy-Preserving Machine Learning in JAX☆93Updated 9 months ago
- code for model-targeted poisoning☆12Updated last year
- ☆80Updated 2 years ago
- Code for Auditing DPSGD☆37Updated 3 years ago
- ☆18Updated 2 years ago
- Code for paper "Robustness of Bayesian Neural Networks to Gradient-Based Attacks"☆17Updated last year
- ☆31Updated 7 months ago
- Code for "Differential Privacy Has Disparate Impact on Model Accuracy" NeurIPS'19☆34Updated 3 years ago
- CaPC is a method that enables collaborating parties to improve their own local heterogeneous machine learning models in a setting where b…☆26Updated 3 years ago
- Provable Robustness of ReLU networks via Maximization of Linear Regions [AISTATS 2019]☆32Updated 4 years ago
- A fast algorithm to optimally compose privacy guarantees of differentially private (DP) mechanisms to arbitrary accuracy.☆73Updated last year
- Github pages backend for https://differentialprivacy.org☆26Updated this week
- Code for fast dpsgd implementations in JAX/TF☆59Updated 2 years ago
- ☆49Updated 4 years ago
- Code for the paper "Adversarial Training and Robustness for Multiple Perturbations", NeurIPS 2019☆47Updated 2 years ago
- [ICML'20] Multi Steepest Descent (MSD) for robustness against the union of multiple perturbation models.☆26Updated 8 months ago
- Official implementation for Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds (NeurIPS, 2021).☆23Updated 2 years ago
- Computationally friendly hyper-parameter search with DP-SGD☆24Updated 2 months ago
- ☆22Updated 2 years ago
- Code for the CSF 2018 paper "Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting"☆38Updated 6 years ago
- A library for running membership inference attacks against ML models☆142Updated 2 years ago
- ☆38Updated 3 years ago
- This project's goal is to evaluate the privacy leakage of differentially private machine learning models.☆131Updated 2 years ago
- Source code for "Neural Anisotropy Directions"☆15Updated 4 years ago
- InstaHide: Instance-hiding Schemes for Private Distributed Learning☆50Updated 4 years ago
- Privacy Risks of Securing Machine Learning Models against Adversarial Examples☆44Updated 5 years ago
- ☆29Updated 3 years ago
- ☆32Updated 7 years ago
- ☆22Updated 3 years ago
- Systematic Evaluation of Membership Inference Privacy Risks of Machine Learning Models☆125Updated 11 months ago