mtsang / archipelago
Official Code Repo for the Paper: "How does This Interaction Affect Me? Interpretable Attribution for Feature Interactions", In NeurIPS 2020
☆37Updated 2 years ago
Related projects ⓘ
Alternatives and complementary repositories for archipelago
- This is the official implementation for the paper "Learning to Scaffold: Optimizing Model Explanations for Teaching"☆19Updated 2 years ago
- ☆86Updated last year
- Code for paper "Search Methods for Sufficient, Socially-Aligned Feature Importance Explanations with In-Distribution Counterfactuals"☆16Updated 2 years ago
- ☆27Updated last year
- Learning the Difference that Makes a Difference with Counterfactually-Augmented Data☆168Updated 3 years ago
- Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling☆30Updated 3 years ago
- Implementation of experiments in paper "Learning from Rules Generalizing Labeled Exemplars" to appear in ICLR2020 (https://openreview.net…☆49Updated last year
- Code for "Rissanen Data Analysis: Examining Dataset Characteristics via Description Length" by Ethan Perez, Douwe Kiela, and Kyungyhun Ch…☆35Updated 3 years ago
- ☆63Updated 4 years ago
- Source code for "Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models", ICLR 2020.☆30Updated 4 years ago
- Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning (AISTATS 2022 Oral)☆40Updated 2 years ago
- Code for "Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?"☆44Updated 10 months ago
- ☆26Updated 2 years ago
- Code for EMNLP 2019 paper "Attention is not not Explanation"☆57Updated 3 years ago
- This is a repository with the code for the EMNLP 2020 paper "Information-Theoretic Probing with Minimum Description Length"☆69Updated 3 months ago
- Code for paper "When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data"☆14Updated 3 years ago
- OOD Generalization and Detection (ACL 2020)☆61Updated 4 years ago
- ☆50Updated last year
- Pytorch implementation of DiffMask☆55Updated last year
- diagNNose is a Python library that facilitates a broad set of tools for analysing hidden activations of neural models.☆81Updated last year
- ☆24Updated 3 years ago
- Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)☆125Updated 3 years ago
- Interpretable Neural Predictions with Differentiable Binary Variables☆84Updated 3 years ago
- ☆87Updated 2 years ago
- This code accompanies the paper "Information-Theoretic Probing for Linguistic Structure" published in ACL 2020.☆20Updated 4 years ago
- A Diagnostic Study of Explainability Techniques for Text Classification☆67Updated 4 years ago
- Combating hidden stratification with GEORGE☆62Updated 3 years ago
- Compositional Explanations of Neurons, NeurIPS 2020 https://arxiv.org/abs/2006.14032☆25Updated 3 years ago
- ☆63Updated 2 years ago
- ☆11Updated 2 years ago