15 citations · 34 across the 17 of their papers we have counts for
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stat.ML2018★ 11 cited
Safe RuleFit: Learning Optimal Sparse Rule Model by Meta Safe Screening
Hiroki Kato, Hiroyuki Hanada, Ichiro Takeuchi
We consider the problem of learning a sparse rule model, a prediction model in the form of a sparse linear combination of rules, where a rule is an indicator function defined over…
stat.ML2018
Interval-based Prediction Uncertainty Bound Computation in Learning with Missing Values
Hiroyuki Hanada, Toshiyuki Takada, Jun Sakuma +1
The problem of machine learning with missing values is common in many areas. A simple approach is to first construct a dataset without missing values simply by discarding instances…