4 papers
Multiple Randomization Designs: Estimation and Inference with Interference
Lorenzo Masoero, Suhas Vijaykumar, Thomas Richardson +5
Classical designs of randomized experiments, going back to Fisher and Neyman in the 1930s still dominate practice even in online experimentation. However, such designs are of limit…
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…
Empirical Bayes for Large-scale Randomized Experiments: a Spectral Approach
F. Richard Guo, James McQueen, Thomas S. Richardson
Large-scale randomized experiments, sometimes called A/B tests, are increasingly prevalent in many industries. Though such experiments are often analyzed via frequentist -tests,…
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,…