19 citations · 100 across the 32 of their papers we have counts for
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cs.LG2019★ 17 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…
math.ST2019★ 19 cited
Fast rates for empirical risk minimization over càdlàg functions with bounded sectional variation norm
Aurélien F. Bibaut, Mark J. van der Laan
Empirical risk minimization over classes functions that are bounded for some version of the variation norm has a long history, starting with Total Variation Denoising (Rudin et al.…