2 citations · 5 across the 4 of their papers we have counts for
5 papers
Why Machine Learning Cannot Ignore Maximum Likelihood Estimation
Mark J. van der Laan, Sherri Rose
The growth of machine learning as a field has been accelerating with increasing interest and publications across fields, including statistics, but predominantly in computer science…
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…
One-step TMLE for targeting cause-specific absolute risks and survival curves
Helene C. W. Rytgaard, Mark J. van der Laan
This paper considers one-step targeted maximum likelihood estimation method for general competing risks and survival analysis settings where event times take place on the positive…
Rate-adaptive model selection over a collection of black-box contextual bandit algorithms
Aurélien F. Bibaut, Antoine Chambaz, Mark J. van der Laan
We consider the model selection task in the stochastic contextual bandit setting. Suppose we are given a collection of base contextual bandit algorithms. We provide a master algori…
Robust inference on the average treatment effect using the outcome highly adaptive lasso
Cheng Ju, David Benkeser, Mark J. van der Laan
Many estimators of the average effect of a treatment on an outcome require estimation of the propensity score, the outcome regression, or both. It is often beneficial to utilize fl…