8 citations · 21 across the 9 of their papers we have counts for
9 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…
Leveraging covariate adjustments at scale in online A/B testing
Lorenzo Masoero, Doug Hains, James McQueen
Companies offering web services routinely run randomized online experiments to estimate the causal impact associated with the adoption of new features and policies on key performan…
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…
Multiple Randomization Designs: Estimation and Inference with Interference
Lorenzo Masoero, Suhas Vijaykumar, Thomas Richardson +5
Completely randomized experiments, originally developed by Fisher and Neyman in the 1930s, are still widely used in practice, even in online experimentation. However, such designs…
A Bayesian Model for Online Activity Sample Sizes
Thomas Richardson, Yu Liu, James McQueen +1
In many contexts it is useful to predict the number of individuals in some population who will initiate a particular activity during a given period. For example, the number of user…
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,…
Improved graph Laplacian via geometric self-consistency
Dominique Perrault-Joncas, Marina Meila
We address the problem of setting the kernel bandwidth used by Manifold Learning algorithms to construct the graph Laplacian. Exploiting the connection between manifold geometry, r…