no code implementations • 20 Dec 2023 • Yu Liu, Runzhe Wan, James McQueen, Doug Hains, Jinxiang Gu, Rui Song
The selection of the assumed effect size (AES) critically determines the duration of an experiment, and hence its accuracy and efficiency.
no code implementations • 21 Jun 2023 • Alberto Abadie, Anish Agarwal, Guido Imbens, Siwei Jia, James McQueen, Serguei Stepaniants
Business/policy decisions are often based on evidence from randomized experiments and observational studies.
no code implementations • 2 Apr 2023 • Runzhe Wan, Yu Liu, James McQueen, Doug Hains, Rui Song
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.
no code implementations • 23 Nov 2021 • Thomas Richardson, Yu Liu, James McQueen, Doug Hains
Given observations on the number of unique users participating in an initial period, we present a simple but novel Bayesian method for predicting the number of additional individuals who will participate during a subsequent period.
no code implementations • NeurIPS 2016 • James McQueen, Marina Meila, Dominique Joncas
Many manifold learning algorithms aim to create embeddings with low or no distortion (i. e. isometric).
1 code implementation • 9 Mar 2016 • James McQueen, Marina Meila, Jacob VanderPlas, Zhongyue Zhang
Manifold Learning is a class of algorithms seeking a low-dimensional non-linear representation of high-dimensional data.