/iml

iml

https://ai.stanford.edu/blog/causal-abstraction/?utm_campaign=Artificial%2BIntelligence%2BWeekly&utm_medium=email&utm_source=Artificial_Intelligence_Weekly_301

https://github.com/interpretml/kdd2022-tutorial

https://github.com/SelfExplainML/PiML-Toolbox#Example

https://scholar.google.com/citations?hl=en&user=pcCS60IAAAAJ&view_op=list_works&sortby=pubdate

https://leanpub.com/interpretable-machine-learning

https://github.com/yangarbiter/robust-local-lipschitz

https://github.com/interpretml/interpret

https://mlu.red/muse/52906366310.html

https://medium.com/plotly/building-and-deploying-explainable-ai-dashboards-using-dash-and-shap-8e0a0a45beb6

https://github.com/plotly/dash-sample-apps/tree/master/apps/dash-cuml-umap

https://github.com/plotly/dash-sample-apps/tree/master/apps/dash-cuml-umap

https://interpret.ml/

automl

https://www.kaggle.com/sukanthen/automl-for-all-pycaret-and-tpot

https://docs.ray.io/en/master/tune/tutorials/tune-xgboost.html

https://interpret.ml/

https://www.vanderschaar-lab.com/automl-powering-the-new-human-machine-learning-ecosystem/

https://mlflow.org/