21 citations · 23 across the 4 of their papers we have counts for
6 papers
Exploring the Whole Rashomon Set of Sparse Decision Trees
Rui Xin, Chudi Zhong, Zhi Chen +3
In any given machine learning problem, there may be many models that could explain the data almost equally well. However, most learning algorithms return only one of these models,…
Efficient Constrained Pattern Mining Using Dynamic Item Ordering for Explainable Classification
Hiroaki Iwashita, Takuya Takagi, Hirofumi Suzuki +3
Learning of interpretable classification models has been attracting much attention for the last few years. Discovery of succinct and contrasting patterns that can highlight the dif…
BRPO: Batch Residual Policy Optimization
Sungryull Sohn, Yinlam Chow, Jayden Ooi +4
In batch reinforcement learning (RL), one often constrains a learned policy to be close to the behavior (data-generating) policy, e.g., by constraining the learned action distribut…
Online Algorithms for Constructing Linear-size Suffix Trie
Diptarama Hendrian, Takuya Takagi, Shunsuke Inenaga
The suffix trees are fundamental data structures for various kinds of string processing. The suffix tree of a string of length has nodes and edges, and the string la…
Multi Instance Learning For Unbalanced Data
Mark Kozdoba, Edward Moroshko, Lior Shani +4
In the context of Multi Instance Learning, we analyze the Single Instance (SI) learning objective. We show that when the data is unbalanced and the family of classifiers is suffici…
MR-RePair: Grammar Compression based on Maximal Repeats
Isamu Furuya, Takuya Takagi, Yuto Nakashima +3
We analyze the grammar generation algorithm of the RePair compression algorithm and show the relation between a grammar generated by RePair and maximal repeats. We reveal that RePa…