provably-robust-boosting

borealisai/advertorch1.3K

Robustness tester

A toolbox for researching and evaluating robustness against attacks on machine learning models

guanghelee/neurips19-certificates-of-robustness17

Robustness framework

Provides a framework for computing tight certificates of adversarial robustness for randomly smoothed classifiers.

eth-sri/diffai219

Robust network trainer

Trains neural networks to be provably robust against adversarial examples using abstract interpretation techniques.

robustbench/robustbench682

Adversarial benchmarking tool

A standardized benchmark for measuring the robustness of machine learning models against adversarial attacks

hendrycks/robustness1K

Model robustness testing

Evaluates and benchmarks the robustness of deep learning models to various corruptions and perturbations in computer vision tasks.

stanfordmlgroup/ngboost1.7K

Boosting library

A Python library implementing a machine learning boosting framework with probabilistic prediction capabilities

jinlow/forust68

Gradient booster

A package implementing a lightweight gradient boosted decision tree algorithm

chenhongge/robusttrees67

Robust decision trees

An implementation of robust decision tree based models against adversarial examples using the XGBoost framework.

charliermarsh/online_boosting65

Online Boosting Library

A suite of algorithms and weak learners for the online learning setting in machine learning

yunqing-me/attackvlm165

Attack framework

An adversarial attack framework on large vision-language models

chong-z/tree-ensemble-attack22

Adversarial attackers

An approach to create adversarial examples for tree-based ensemble models

guillermo-navas-palencia/optbinning460

Binning algorithm

Optimal binning for binary, continuous and multiclass target types with constraints

bsharchilev/influence_boosting67

Influence finder

This repository implements methods to find influential training samples in Gradient Boosted Decision Trees ensembles

illidanlab/fedrbn26

Robustness Sharing

An implementation of Federated Robustness Propagation in PyTorch to share robustness across heterogeneous federated learning users.

zfancy/sfat28

Heterogeneity fixer

Combating heterogeneity in federated learning by combining adversarial training with client-wise slack during aggregation