75 citations · 459 across the 65 of their papers we have counts for
5 papers · 2 filters
Safe Grid Search with Optimal Complexity
Eugene Ndiaye, Tam Le, Olivier Fercoq +2
Popular machine learning estimators involve regularization parameters that can be challenging to tune, and standard strategies rely on grid search for this task. In this paper, we…
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…
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…
Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator
Makoto Yamada, Denny Wu, Yao-Hung Hubert Tsai +3
Measuring divergence between two distributions is essential in machine learning and statistics and has various applications including binary classification, change point detection,…
Safe Triplet Screening for Distance Metric Learning
Tomoki Yoshida, Ichiro Takeuchi, Masayuki Karasuyama
We study safe screening for metric learning. Distance metric learning can optimize a metric over a set of triplets, each one of which is defined by a pair of same class instances a…