1 citations · 1 across the 4 of their papers we have counts for
4 papers
Practical Policy Optimization with Personalized Experimentation
Mia Garrard, Hanson Wang, Ben Letham +9
Many organizations measure treatment effects via an experimentation platform to evaluate the casual effect of product variations prior to full-scale deployment. However, standard e…
Scalable End-to-End ML Platforms: from AutoML to Self-serve
Igor L. Markov, Pavlos A. Apostolopoulos, Mia R. Garrard +8
ML platforms help enable intelligent data-driven applications and maintain them with limited engineering effort. Upon sufficiently broad adoption, such platforms reach economies of…
Interpretable Personalized Experimentation
Han Wu, Sarah Tan, Weiwei Li +7
Black-box heterogeneous treatment effect (HTE) models are increasingly being used to create personalized policies that assign individuals to their optimal treatments. However, they…
Looper: An end-to-end ML platform for product decisions
Igor L. Markov, Hanson Wang, Nitya Kasturi +16
Modern software systems and products increasingly rely on machine learning models to make data-driven decisions based on interactions with users, infrastructure and other systems.…