Jianbo-Lab / L2X
☆125Updated 3 years ago
Alternatives and similar repositories for L2X:
Users that are interested in L2X are comparing it to the libraries listed below
- Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.☆131Updated 4 years ago
- Supervised Local Modeling for Interpretability☆28Updated 6 years ago
- Code and data for the experiments in "On Fairness and Calibration"☆51Updated 3 years ago
- Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)☆128Updated 3 years ago
- Codebase for "Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains Its Predictions" (to appear in AA…☆75Updated 7 years ago
- ☆134Updated 5 years ago
- ☆50Updated 2 years ago
- ☆87Updated 5 years ago
- Code for paper EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE☆39Updated last year
- Tools for training explainable models using attribution priors.☆124Updated 4 years ago
- Codebase for INVASE: Instance-wise Variable Selection - 2019 ICLR☆60Updated 4 years ago
- Keras implementation for DASP: Deep Approximate Shapley Propagation (ICML 2019)☆61Updated 5 years ago
- Experiments for AAAI anchor paper☆63Updated 7 years ago
- code release for Representer point Selection for Explaining Deep Neural Network in NeurIPS 2018☆67Updated 3 years ago
- Algorithms for abstention, calibration and domain adaptation to label shift.☆36Updated 4 years ago
- A lightweight implementation of removal-based explanations for ML models.☆59Updated 3 years ago
- Fair Empirical Risk Minimization (FERM)☆37Updated 4 years ago
- Model Agnostic Counterfactual Explanations☆87Updated 2 years ago
- ☆14Updated last year
- Code/figures in Right for the Right Reasons☆55Updated 4 years ago
- Interpretation of Neural Network is Fragile☆36Updated last year
- This repository contains the full code for the "Towards fairness in machine learning with adversarial networks" blog post.☆117Updated 4 years ago
- Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks [NeurIPS 2019]☆50Updated 5 years ago
- [ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples☆67Updated 2 years ago
- Code for our ICML '19 paper: Neural Network Attributions: A Causal Perspective.☆51Updated 3 years ago
- ☆51Updated 4 years ago
- Code for the Structural Agnostic Model (https://arxiv.org/abs/1803.04929)☆52Updated 4 years ago
- Feature Interaction Interpretability via Interaction Detection☆34Updated last year
- Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" ht…☆127Updated 4 years ago
- ☆42Updated 6 years ago