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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.**
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*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.**
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*Xiangnan He, Fuli Feng, Liqiang Nie and Tat-Seng Chua*. WWW’18. [paper](https://www.comp.nus.edu.sg/~xiangnan/papers/www18-tem.pdf)
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1. **Towards Deep Interpretability (MUS-ROVER II): Learning Hierarchical Representations of Tonal Music.** 
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*Haizi Yu, Lav R. Varshney*. ICLR’17. [paper](https://openreview.net/pdf?id=ryhqQFKgl)
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1. **Generating Interpretable Images with Controllable Structure**
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*Scott Reed, Aron van den Oord, Nal Kalchbrenner, Victor Bapst, Matt Botvinick, Nando de Freitas*. ICLR’17. [paper](http://www.scottreed.info/files/iclr2017.pdf)
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1. **Supervised topic models for clinical interpretability.**
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*Hughes et al.*. 2016.[paper](https://arxiv.org/pdf/1612.01678.pdf)
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1. **An Effective and Interpretable Method for Document Classification**
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*Ngo Van Linh, Nguyen Kim Anh, Khoat Than, Chien Nguyen Dang*. KAIS 2016.[paper](http://is.hust.edu.vn/~khoattq/papers/kais-2016.pdf)
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1. **Interpretable probabilistic embeddings: bridging the gap between topic models and neural networks.**
*Anna Potapenko, Artem Popov, and Konstantin Vorontsov*. 2017.[paper](https://arxiv.org/pdf/1711.04154.pdf)
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1. **Interpretable Explanations of Black Boxes by Meaningful Perturbation.** 
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*Fong, Ruth C and Vedaldi, Andrea*.ICCV 2017.[paper](http://openaccess.thecvf.com/content_ICCV_2017/papers/Fong_Interpretable_Explanations_of_ICCV_2017_paper.pdf)