saradindusengupta/Interpretable-ML-in-production

This repository contains example code and notebooks for the talk given at GDG Bangalore Cloud Community Day, 2023

Interpretable ML in production

This repository contains example code and notebooks for the talk given at GDG Bangalore Cloud Community Day, 2023.

Presentation Slides

References

1. Zachary C. Lipton, “The Mythos of Model Interpretability”, 2016, [https://arxiv.org/abs/1606.03490]
2. Finale Doshi-Velez, Been Kim, “Towards A Rigorous Science of Interpretable Machine Learning”, 2017, [https://arxiv.org/abs/1702.08608]
3. S. Ghanta et al., "Interpretability and Reproducability in Production Machine Learning Applications," 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA, 2018, pp. 658-664, doi: 10.1109/ICMLA.2018.00105.
4. Lou,Yin, Caruana, Rich, and Gehrke, Johannes. “Intelligible models for classification and regression”.In KDD, 2012.
5. Dragan, AncaD, Lee, KentonCT, and Srinivasa, Siddhartha S., “Legibility and predictability of robot motion. In Human-Robot Interaction(HRI), 2013 8th ACM/IEEE International Conferenceon. IEEE, 2013.
6. Ribeiro et. al. 2016[https://arxiv.org/abs/1602.04938]
7. Ribeiro et al. 2018[https://homes.cs.washington.edu/~marcotcr/aaai18.pdf]
8. Lakkaraju et. al. 2019 [https://dl.acm.org/doi/10.1145/3306618.3314229]
9. Machine Learning Explainability Workshop, Stanford University [https://youtube.com/playlist?list=PLoROMvodv4rPh6wa6PGcHH6vMG9sEIPxL]
10. Lundberg, S., & Lee, S. (2017). A Unified Approach to Interpreting Model Predictions. ArXiv . /abs/1705.07874 [https://arxiv.org/abs/1705.07874]
11. J. Wexler, M. Pushkarna, T. Bolukbasi, M. Wattenberg, F. Viégas and J. Wilson, "The What-If Tool: Interactive Probing of Machine Learning Models," in IEEE Transactions on Visualization and Computer Graphics , vol. 26, no. 1, pp. 56-65, Jan. 2020, doi: 10.1109/TVCG.2019.2934619.[https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8807255]