kfoofw/applied_learning_articles

Collaborative project for documenting ML/DS learnings.

applied_learning_articles

This repo is a collaborative project between Viswanath Gangavaram and Kenneth Foo to document and apply our learnings on topics such as:

• Bandits algorithms
• Causal Inference
• Heterogenous Treatment Effects/Uplift modelling

List of articles in this repo:

• Uplift Modelling aka Heterogeneous Treatment Effects Evaluation with Criteo Data

List of paper summaries:

• From predictive uplift modeling to prescriptive uplift analytics: A practical approach to treatment optimization while accounting for estimation risk
• Free Lunch! Retrospective Uplift Modeling for Dynamic Promotions Recommendation within ROI Constraints
• LORE: A large-scale offer recommendation engine through the lens of an online subscription service

Please feel free to leave any comments/issues or connect with us on LinkedIn!

• Viswanath Gangavaram
• Kenneth Foo