benedikthoeltgen / DeDUCE
☆8Updated 2 years ago
Related projects ⓘ
Alternatives and complementary repositories for DeDUCE
- Quantile risk minimization☆24Updated 3 months ago
- Code for our paper☆12Updated 2 years ago
- Official repository for the AAAI-21 paper 'Explainable Models with Consistent Interpretations'☆18Updated 2 years ago
- Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"☆36Updated 6 months ago
- Python code for NeurIPS 2018 paper "Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models"☆22Updated 5 years ago
- Code for "Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties"☆18Updated 3 years ago
- Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI☆52Updated 2 years ago
- A pytorch implemention of the Explainable AI work 'Contrastive layerwise relevance propagation (CLRP)'☆17Updated 2 years ago
- Experiments on meta-learning algorithms to solve few-shot domain adaptation☆10Updated 3 years ago
- Explanation Optimization☆13Updated 4 years ago
- A lightweight implementation of removal-based explanations for ML models.☆57Updated 3 years ago
- Python implementation for evaluating explanations presented in "On the (In)fidelity and Sensitivity for Explanations" in NeurIPS 2019 for…☆25Updated 2 years ago
- CEML - Counterfactuals for Explaining Machine Learning models - A Python toolbox☆42Updated 3 months ago
- Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"☆25Updated 3 years ago
- Code for our ICML '19 paper: Neural Network Attributions: A Causal Perspective.☆51Updated 3 years ago
- Code to reproduce the experimental results from the paper "Active Invariant Causal Prediction: Experiment Selection Through Stability", b…☆19Updated last year
- The official codebase for Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders paper (UAI'21).☆16Updated 3 years ago
- Codebase for "Clairvoyance: a Unified, End-to-End AutoML Pipeline for Medical Time Series"☆13Updated 4 years ago
- NoiseGrad (and its extension NoiseGrad++) is a method to enhance explanations of artificial neural networks by adding noise to model weig…☆21Updated last year
- Local explanations with uncertainty 💐!☆39Updated last year
- An Empirical Framework for Domain Generalization In Clinical Settings☆27Updated 2 years ago
- Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable ? (ICML 2021)☆27Updated last year
- A Library for Minimax Risk Classifiers☆31Updated 4 months ago
- Rule Extraction Methods for Interactive eXplainability☆41Updated 2 years ago
- ☆15Updated last year
- Data-SUITE: Data-centric identification of in-distribution incongruous examples (ICML 2022)☆9Updated last year
- CME: Concept-based Model Extraction☆12Updated 3 years ago
- ☆18Updated 3 years ago
- Code for the paper "Rethinking Importance Weighting for Deep Learning under Distribution Shift".☆27Updated 3 years ago
- Code accompanying the paper "Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers"☆30Updated last year