Nearly Isometric Embedding by Relaxation. - James McQueen; Marina Meila; Dominique Joncas (NIPS, 2016)

megaman: Manifold Learning with Millions of points. - James McQueen; Marina Meila; Jacob VanderPlas; Zhongyue Zhang (CoRR, 2016)

Megaman: Scalable Manifold Learning in Python. - James McQueen; Marina Meila; Jacob VanderPlas; Zhongyue Zhang (J. Mach. Learn. Res., 2016)

Improved Graph Laplacian via Geometric Self-Consistency. - Dominique Joncas; Marina Meila; James McQueen (NIPS, 2017)

Bayesian Sample Size Prediction for Online Activity. - Thomas S. Richardson 001; Yu Liu 080; James McQueen; Doug Hains (CoRR, 2021)

Multiple Randomization Designs. - Patrick Bajari; Brian Burdick; Guido W. Imbens; Lorenzo Masoero; James McQueen; Thomas S. Richardson 001; Ido M. Rosen (CoRR, 2021)

A Bayesian Model for Online Activity Sample Sizes. - Thomas S. Richardson 001; Yu Liu 080; James McQueen; Doug Hains (AISTATS, 2022)

Experimentation Platforms Meet Reinforcement Learning: Bayesian Sequential Decision-Making for Continuous Monitoring. - Runzhe Wan; Yu Liu 080; James McQueen; Doug Hains; Rui Song 006 (KDD, 2023)

Leveraging covariate adjustments at scale in online A/B testing. - Lorenzo Masoero; Doug Hains; James McQueen (CDPD, 2023)

Estimating the Value of Evidence-Based Decision Making. - Alberto Abadie; Anish Agarwal; Guido Imbens; Siwei Jia; James McQueen; Serguei Stepaniants (CoRR, 2023)

Effect Size Estimation for Duration Recommendation in Online Experiments: Leveraging Hierarchical Models and Objective Utility Approaches. - Yu Liu 080; Runzhe Wan; James McQueen; Doug Hains; Jinxiang Gu; Rui Song 006 (AAAI, 2024)

Know When to Fold: Futility-Aware Early Termination in Online Experiments. - Yu Liu 080; Runzhe Wan; Yian Huang; James McQueen; Doug Hains; Jinxiang Gu; Rui Song 006 (WWW (Companion Volume), 2025)