Practical Policy Optimization with Personalized Experimentation

no code implementations • 30 Mar 2023 • Mia Garrard, Hanson Wang, Ben Letham, Shaun Singh, Abbas Kazerouni, Sarah Tan, Zehui Wang, Yin Huang, Yichun Hu, Chad Zhou, Norm Zhou, Eytan Bakshy

Many organizations measure treatment effects via an experimentation platform to evaluate the casual effect of product variations prior to full-scale deployment.

Scalable End-to-End ML Platforms: from AutoML to Self-serve

no code implementations • 27 Feb 2023 • Igor L. Markov, Pavlos A. Apostolopoulos, Mia R. Garrard, Tanya Qie, Yin Huang, Tanvi Gupta, Anika Li, Cesar Cardoso, George Han, Ryan Maghsoudian, Norm Zhou

ML platforms help enable intelligent data-driven applications and maintain them with limited engineering effort.

AutoML

Looper: An end-to-end ML platform for product decisions

no code implementations • 14 Oct 2021 • Igor L. Markov, Hanson Wang, Nitya Kasturi, Shaun Singh, Sze Wai Yuen, Mia Garrard, Sarah Tran, Yin Huang, Zehui Wang, Igor Glotov, Tanvi Gupta, Boshuang Huang, Peng Chen, Xiaowen Xie, Michael Belkin, Sal Uryasev, Sam Howie, Eytan Bakshy, Norm Zhou

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.

Decision Making

Personalization for Web-based Services using Offline Reinforcement Learning

no code implementations • 10 Feb 2021 • Pavlos Athanasios Apostolopoulos, Zehui Wang, Hanson Wang, Chad Zhou, Kittipat Virochsiri, Norm Zhou, Igor L. Markov

Large-scale Web-based services present opportunities for improving UI policies based on observed user interactions.

reinforcement-learning Reinforcement Learning (RL)