research
∙
09/03/2023
Double Clipping: Less-Biased Variance Reduction in Off-Policy Evaluation
"Clipping" (a.k.a. importance weight truncation) is a widely used varian...
share
research
∙
10/15/2022
Fair Effect Attribution in Parallel Online Experiments
A/B tests serve the purpose of reliably identifying the effect of change...
research
∙
05/12/2022
Low-variance estimation in the Plackett-Luce model via quasi-Monte Carlo sampling
The Plackett-Luce (PL) model is ubiquitous in learning-to-rank (LTR) bec...
research
∙
07/28/2021
Ranker-agnostic Contextual Position Bias Estimation
Learning-to-rank (LTR) algorithms are ubiquitous and necessary to explor...
research
∙
10/17/2018
Hierarchical Methods of Moments
Spectral methods of moments provide a powerful tool for learning the par...
research
∙
07/04/2018
Generating Synthetic but Plausible Healthcare Record Datasets
Generating datasets that "look like" given real ones is an interesting t...
research
∙
08/29/2017
