provably-robust-boosting
Robust Boosting Models
Provides provably robust machine learning models against adversarial attacks
Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks [NeurIPS 2019]
50 stars
6 watching
12 forks
Language: Python
last commit: over 4 years ago
Linked from 2 awesome lists
adversarial-attacksboosted-decision-stumpsboosted-treesboostingprovable-defense
Related projects:
Repository | Description | Stars |
---|---|---|
borealisai/advertorch | A toolbox for researching and evaluating robustness against attacks on machine learning models | 1,308 |
guanghelee/neurips19-certificates-of-robustness | Tight certificates of adversarial robustness for randomly smoothed classifiers | 17 |
eth-sri/diffai | Trains neural networks to be provably robust against adversarial examples using abstract interpretation techniques. | 218 |
robustbench/robustbench | A standardized benchmark for measuring the robustness of machine learning models against adversarial attacks | 667 |
hendrycks/robustness | Evaluates and benchmarks the robustness of deep learning models to various corruptions and perturbations in computer vision tasks. | 1,022 |
stanfordmlgroup/ngboost | A Python library implementing a machine learning boosting framework with probabilistic prediction capabilities | 1,654 |
jinlow/forust | A package implementing a lightweight gradient boosted decision tree algorithm | 67 |
chenhongge/robusttrees | An implementation of robust decision tree based models against adversarial examples using the XGBoost framework. | 67 |
charliermarsh/online_boosting | A suite of algorithms and weak learners for the online learning setting in machine learning | 63 |
yunqing-me/attackvlm | An adversarial attack framework on large vision-language models | 161 |
chong-z/tree-ensemble-attack | An approach to create adversarial examples for tree-based ensemble models | 22 |
guillermo-navas-palencia/optbinning | Optimal binning for binary, continuous and multiclass target types with constraints | 457 |
bsharchilev/influence_boosting | This repository implements methods to find influential training samples in Gradient Boosted Decision Trees ensembles | 67 |
illidanlab/fedrbn | An implementation of Federated Robustness Propagation in PyTorch to share robustness across heterogeneous federated learning users. | 26 |
zfancy/sfat | Combating heterogeneity in federated learning by combining adversarial training with client-wise slack during aggregation | 28 |