70 citations · 74 across the 4 of their papers we have counts for
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cs.LG2023★ 1 cited
Learning Action Embeddings for Off-Policy Evaluation
Matej Cief, Jacek Golebiowski, Philipp Schmidt +2
Off-policy evaluation (OPE) methods allow us to compute the expected reward of a policy by using the logged data collected by a different policy. OPE is a viable alternative to run…
cs.LG2023
Variational Boosted Soft Trees
Tristan Cinquin, Tammo Rukat, Philipp Schmidt +2
Gradient boosting machines (GBMs) based on decision trees consistently demonstrate state-of-the-art results on regression and classification tasks with tabular data, often outperfo…