Awesome - Interpretable Machine Leanring Papers/Books

A list of the interpretable machine learning papers. The purpose of this list is to encourage transportation researchers interested in the world of interpretable machine learning.

Background

You can also follow some awesome paper lists, for example, Deep Learning, Deep Vision and Awesome Recurrent Neural Networks, Deep Learning Papers Reading Roadmap.

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Contents

Concepts/Algorithms

• Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning (2018), B. Biggio, and F. Roli [pdf]
• Understanding Black-box Predictions via Influence Functions (2018), P. Koh, and P. Liang [pdf]
• Model Class Reliance: Variable Importance Measures for any Machine Learning Model Class, from the "Rashomon" Perspective (2018), A. Fisher et al. [pdf]
• Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models (2018), D. Apley [pdf]
• Understanding Black-box Predictions via Influence Functions (2017), P. Koh, and P. Liang [pdf]
• Practical Black-Box Attacks against Machine Learning (2017), N. Papernot et al. [pdf]
• Inverse Classification for Comparison-based Interpretability in Machine Learning (2017), T. Laugel et al. [pdf]
• An unexpected unity among methods for interpreting model predictions (2016), S. Lundberg, and S. Lee [pdf]
• Why should i trust you?: Explaining the predictions of any classifier. (2016), M. Ribeiro et al. [pdf]
• Examples are not enough, learn to criticize! criticism for interpretability (2016), P. Koh, and P. Liang [pdf]

Text Data

• "What is relevant in a text document?": An interpretable machine learning approach (2017), L.Arras et al. [pdf]
• Explaining Data-Driven Document Classifications (2014), G. Hinton et al. [pdf]

R Packages

• lime (2018), T. Pedersen [github]
• DALEX (2018), P. Biecek [weblink]
• lightgbmExplainer (2018), P. Biecek [github]
• randomForestExplainer (2018), A. Paluszynska, and P. Biecek [github]

Gitbook

• A Guide for Making Black Box Models Explainable. (2018), C. Molnar [gitbook]