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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...
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10/15/2022
Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model
A critical need for industrial recommender systems is the ability to eva...
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10/15/2022
Fair Effect Attribution in Parallel Online Experiments
A/B tests serve the purpose of reliably identifying the effect of change...
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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...
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07/28/2021
Ranker-agnostic Contextual Position Bias Estimation
Learning-to-rank (LTR) algorithms are ubiquitous and necessary to explor...
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04/28/2020
A Linear Bandit for Seasonal Environments
Contextual bandit algorithms are extremely popular and widely used in re...
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03/30/2020
Non-exchangeable feature allocation models with sublinear growth of the feature sizes
Feature allocation models are popular models used in different applicati...
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11/20/2017
