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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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10/10/2019
Distributed Bayesian Computation for Model Choice
We propose a general method for distributed Bayesian model choice, where...
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10/10/2019
Distributed Computation for Marginal Likelihood based Model Choice
We propose a general method for distributed Bayesian model choice, using...
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08/23/2018
Adaptive Tuning Of Hamiltonian Monte Carlo Within Sequential Monte Carlo
Sequential Monte Carlo (SMC) samplers form an attractive alternative to ...
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07/04/2018
Quasi-Monte Carlo Variational Inference
Many machine learning problems involve Monte Carlo gradient estimators. ...
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10/03/2017