robust-machine-learning

Curated list of open source tooling for data-centric AI on unstructured data.

A curated (most recent) list of resources for Learning with Noisy Labels

ST-SSL (STSSL): Spatio-Temporal Self-Supervised Learning for Traffic Flow Forecasting/Prediction

⚔️ Blades: A Unified Benchmark Suite for Attacks and Defenses in Federated Learning

pyDVL is a library of stable implementations of algorithms for data valuation and influence function computation

Reading list for adversarial perspective and robustness in deep reinforcement learning.

A repository contains a collection of resources and papers on Imbalance Learning On Graphs

A project to add scalable state-of-the-art out-of-distribution detection (open set recognition) support by changing two lines of code! Perform efficient inferences (i.e., do not increase inference time) and detection without classification accuracy drop, hyperparameter tuning, or collecting additional data.

A project to add scalable state-of-the-art out-of-distribution detection (open set recognition) support by changing two lines of code! Perform efficient inferences (i.e., do not increase inference time) and detection without classification accuracy drop, hyperparameter tuning, or collecting additional data.

Robust Reinforcement Learning with the Alternating Training of Learned Adversaries (ATLA) framework

Robust Reinforcement Learning with the Alternating Training of Learned Adversaries (ATLA) framework

Randomized Smoothing of All Shapes and Sizes (ICML 2020).

Randomized Smoothing of All Shapes and Sizes (ICML 2020).

A project to improve out-of-distribution detection (open set recognition) and uncertainty estimation by changing a few lines of code in your project! Perform efficient inferences (i.e., do not increase inference time) without repetitive model training, hyperparameter tuning, or collecting additional data.

A project to improve out-of-distribution detection (open set recognition) and uncertainty estimation by changing a few lines of code in your project! Perform efficient inferences (i.e., do not increase inference time) without repetitive model training, hyperparameter tuning, or collecting additional data.

This is the code for our paper `Robust Federated Learning with Attack-Adaptive Aggregation' accepted by FTL-IJCAI'21.

This is the code for our paper `Robust Federated Learning with Attack-Adaptive Aggregation' accepted by FTL-IJCAI'21.

The code of AAAI-21 paper titled "Defending against Backdoors in Federated Learning with Robust Learning Rate".

[ICLR 2023] "Combating Exacerbated Heterogeneity for Robust Models in Federated Learning"

The code of AAAI-21 paper titled "Defending against Backdoors in Federated Learning with Robust Learning Rate".