... | ... | @@ -4,5 +4,21 @@ |
|
|
* [Reinforcement Learning for NLP and Text](Re-inforcement-Learning-for-NLP-and-Text)
|
|
|
* [Tutorials and Introductory remarks](Tutorials-and-Introductory-remarks)
|
|
|
|
|
|
# Resources
|
|
|
|
|
|
## Tutorials
|
|
|
|Title|Venue|Authors|slides|
|
|
|
|-----|-----|-------|------|
|
|
|
|Interpretable ML: The fuss, the concrete and the questions|ICML 2017|Kim and Doshi-Velez|[link](https://people.csail.mit.edu/beenkim/papers/BeenK_FinaleDV_ICML2017_tutorial.pdf)|
|
|
|
|Tutorial on XKDD| XKDD Workshop ECML 2018|Monreale et al.|[link](https://kdd.isti.cnr.it/xkdd2019/pkdd2019xkdd_tutorial_last.pdf)|
|
|
|
|Explainable AI in Industry|KDD 2019|Geyik et al., LinkedIn|[link](https://www.slideshare.net/KrishnaramKenthapadi/explainable-ai-in-industry-kdd-2019-tutorial)|
|
|
|
|On Explainable AI:From Theory to Motivation, Applications and Limitations|AAAI 2019|many|[link](https://xaitutorial2019.github.io/#)|
|
|
|
|
|
|
## Overviews, Surveys, Reviews
|
|
|
|Title|Author|link|
|
|
|
|-----|------|----|
|
|
|
|The Mythos of Model Interpretability|Lipton|[arxiv](https://arxiv.org/abs/1606.03490)|
|
|
|
|Towards a Rigorous Science of Interpretable Machine Learning|Doshi-Velez and Kim|[arxiv](https://arxiv.org/abs/1702.08608)|
|
|
|
|A Survey of Methods for Explaining Black Box Models| Guidotti et al.|[arxiv](https://arxiv.org/abs/1802.01933)|
|
|
|
|
|
|
|