8 citations · 21 across the 9 of their papers we have counts for
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cs.LG2023
Effect Size Estimation for Duration Recommendation in Online Experiments: Leveraging Hierarchical Models and Objective Utility Approaches
Yu Liu, Runzhe Wan, James McQueen +3
The selection of the assumed effect size (AES) critically determines the duration of an experiment, and hence its accuracy and efficiency. Traditionally, experimenters determine AE…
cs.LG2023★ 2 cited
Experimentation Platforms Meet Reinforcement Learning: Bayesian Sequential Decision-Making for Continuous Monitoring
Runzhe Wan, Yu Liu, James McQueen +2
With the growing needs of online A/B testing to support the innovation in industry, the opportunity cost of running an experiment becomes non-negligible. Therefore, there is an inc…
cs.LG2016★ 8 cited
megaman: Manifold Learning with Millions of points
James McQueen, Marina Meila, Jacob VanderPlas +1
Manifold Learning is a class of algorithms seeking a low-dimensional non-linear representation of high-dimensional data. Thus manifold learning algorithms are, at least in theory,…