A list of research papers of explainable machine learning.
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Updated
Jun 25, 2021
A list of research papers of explainable machine learning.
A School for All Seasons on Trustworthy Machine Learning
Explanation-guided boosting of machine learning evasion attacks.
Explainable Debugger for Black-box Machine Learning Models
[ICCV2021 Oral] Fooling LiDAR by Attacking GPS Trajectory
a tool for comparing the predictions of any text classifiers
[ICML2022 Long Talk] Official Pytorch implementation of "To Smooth or Not? When Label Smoothing Meets Noisy Labels"
Morphence: An implementation of a moving target defense against adversarial example attacks demonstrated for image classification models trained on MNIST and CIFAR10.
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 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 train your model from scratch or fine-tune a pretrained model using the losses provided in this library to improve out-of-distribution detection and uncertainty estimation performances. Calibrate your model to produce enhanced uncertainty estimations. Detect out-of-distribution data using the defined score type and threshold.
Code from PLDI '21 paper "Provable Repair of Deep Neural Networks."
Data-SUITE: Data-centric identification of in-distribution incongruous examples (ICML 2022)
SyReNN: Symbolic Representations for Neural Networks
Papers and online resources related to machine learning fairness
[Findings of EMNLP 2022] Holistic Sentence Embeddings for Better Out-of-Distribution Detection
Trustworthy AI method based on Dempster-Shafer theory - application to fetal brain 3D T2w MRI segmentation
Framework for Adversarial Malware Evaluation.
PyTorch package to train and audit ML models for Individual Fairness
MERLIN is a global, model-agnostic, contrastive explainer for any tabular or text classifier. It provides contrastive explanations of how the behaviour of two machine learning models differs.
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