activity
20182022
most citedMore Efficient Off-Policy Evaluation through Regularized Targeted Learning

17 citations · 27 across the 5 of their papers we have counts for

collaborators

7 papers

stat.ME2022

Adaptive Sequential Surveillance with Network and Temporal Dependence

Ivana Malenica, Jeremy R. Coyle, Mark J. van der Laan +1

Strategic test allocation plays a major role in the control of both emerging and existing pandemics (e.g., COVID-19, HIV). Widespread testing supports effective epidemic control by…

stat.ME20211 cited

Evaluating the Robustness of Targeted Maximum Likelihood Estimators via Realistic Simulations in Nutrition Intervention Trials

Haodong Li, Sonali Rosete, Jeremy Coyle +9

Several recently developed methods have the potential to harness machine learning in the pursuit of target quantities inspired by causal inference, including inverse weighting, dou…

stat.ML20211 cited

Personalized Online Machine Learning

Ivana Malenica, Rachael V. Phillips, Romain Pirracchio +3

In this work, we introduce the Personalized Online Super Learner (POSL) -- an online ensembling algorithm for streaming data whose optimization procedure accommodates varying degre…

math.ST2021

Adaptive Sequential Design for a Single Time-Series

Ivana Malenica, Aurelien Bibaut, Mark J. van der Laan

The current work is motivated by the need for robust statistical methods for precision medicine; as such, we address the need for statistical methods that provide actionable infere…

stat.ME20208 cited

Targeting Learning: Robust Statistics for Reproducible Research

Jeremy R. Coyle, Nima S. Hejazi, Ivana Malenica +9

Targeted Learning is a subfield of statistics that unifies advances in causal inference, machine learning and statistical theory to help answer scientifically impactful questions w…

cs.LG201917 cited

More Efficient Off-Policy Evaluation through Regularized Targeted Learning

Aurélien F. Bibaut, Ivana Malenica, Nikos Vlassis +1

We study the problem of off-policy evaluation (OPE) in Reinforcement Learning (RL), where the aim is to estimate the performance of a new policy given historical data that may have…