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## Must-read papers on Interpretability and Explanations.
NRL: network representation learning. NE: network embedding.


We release [InterpretMe]

### Survey papers:

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1. **Jaspreet's Master Piece**
*Jaspreet Singh* 2019. [paper](https://arxiv.org/pdf/xxx.pdf)
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### Journal and Conference papers:

1. **Towards a rigorous science of interpretable machine learning.**
*Finale Doshi-Velez and Been Kim.*  2017. [paper]

1. **Streaming weak submodularity: Interpreting neural networks on the fly.**
*Ethan R Elenberg, Alexandros G Dimakis, Moran Feldman, and Amin Karbasi*. 2017 [paper](https://arxiv.org/pdf/1703.02647).

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1. **Interpretable explanations of black boxes by meaningful perturbation.**
*Ruth C Fong and Andrea Vedaldi.*.CVPR 2017. [paper]
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1. **Supervised topic models for clinical interpretability.**
*Michael C Hughes, Huseyin Melih Elibol, Thomas McCoy, Roy Perlis, and Finale Doshi-Velez.*.2016. [paper](https://arxiv.org/pdf/1612.01678)
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1. **A unified approach to interpreting model predictions.**
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*Scott Lundberg and Su-In Lee.*.2016. [paper](https://arxiv.org/pdf/1705.07874)
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1. **A human-grounded evaluation benchmark for local explanations of machine learning.**
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*Sina Mohseni and Eric D Ragan.*.2018. [paper](https://arxiv.org/pdf/1801.05075).
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1. **Anchors: High-precision model-agnostic explanations.**
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*Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin.*.AAAI 2018. [paper]
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1. **Right for the right reasons: Training differentiable models by constraining their explanations.**
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*Andrew Slavin Ross, Michael C. Hughes, and Finale Doshi-Velez.*.IJCAI 2018. [paper](https://doi.org/10.24963/ijcai.2017/371)
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1. **Sharing Deep Neural Network Models with Interpretation.**
*Huijun Wu, Chen Wang, Jie Yin, Kai Lu and Liming Zhu*. WWW’18.  [paper](https://doi.org/10.24963/ijcai.2017/371)

1. **TEM:Tree-enhanced Embedding Model for Explainable Recommendation Xiang Wang.**
*Xiangnan He, Fuli Feng, Liqiang Nie and Tat-Seng Chua*. WWW’18. [paper]

1. **Towards Deep Interpretability (MUS-ROVER II): Learning Hierarchical Representations of Tonal Music.** 
*Haizi Yu, Lav R. Varshney*. ICLR’17. [paper]

1. **Generating Interpretable Images with Controllable Structure**
*Scott Reed, Aron van den Oord, Nal Kalchbrenner, Victor Bapst, Matt Botvinick, Nando de Freitas*. ICLR’17. [paper]

1. **Supervised topic models for clinical interpretability.**
*Hughes et al.*. 2016.

1. **An Effective and Interpretable Method for Document Classification**
*Ngo Van Linh, Nguyen Kim Anh, Khoat Than, Chien Nguyen Dang*.

1. **Interpretable probabilistic embeddings: bridging the gap between topic models and neural networks**
*Anna Potapenko, Artem Popov, and Konstantin Vorontsov*.

1. **Interpretable Explanations of Black Boxes by Meaningful Perturbation.** 
*Fong, Ruth C and Vedaldi, Andrea*.