1 citations · 2 across the 5 of their papers we have counts for
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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…
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.…
Personalization for Web-based Services using Offline Reinforcement Learning
Pavlos Athanasios Apostolopoulos, Zehui Wang, Hanson Wang +4
Large-scale Web-based services present opportunities for improving UI policies based on observed user interactions. We address challenges of learning such policies through model-fr…
Predictive Precompute with Recurrent Neural Networks
Hanson Wang, Zehui Wang, Yuanyuan Ma
In both mobile and web applications, speeding up user interface response times can often lead to significant improvements in user engagement. A common technique to improve responsi…