24 citations · 60 across the 10 of their papers we have counts for
12 papers
Algorithmic Recourse with Missing Values
Kentaro Kanamori, Takuya Takagi, Ken Kobayashi +1
This paper proposes a new framework of algorithmic recourse (AR) that works even in the presence of missing values. AR aims to provide a recourse action for altering the undesired…
Linear Time Online Algorithms for Constructing Linear-size Suffix Trie
Diptarama Hendrian, Takuya Takagi, Shunsuke Inenaga +2
The suffix trees are fundamental data structures for various kinds of string processing. The suffix tree of a text string of length has nodes and edges, and the stri…
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,…
TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization
Zijie J. Wang, Chudi Zhong, Rui Xin +5
Given thousands of equally accurate machine learning (ML) models, how can users choose among them? A recent ML technique enables domain experts and data scientists to generate a co…
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…
Linear-size CDAWG: new repetition-aware indexing and grammar compression
Takuya Takagi, Keisuke Goto, Yuta Fujishige +2
In this paper, we propose a novel approach to combine \emph{compact directed acyclic word graphs} (CDAWGs) and grammar-based compression. This leads us to an efficient self-index,…
Packed Compact Tries: A Fast and Efficient Data Structure for Online String Processing
Takuya Takagi, Shunsuke Inenaga, Kunihiko Sadakane +1
In this paper, we present a new data structure called the packed compact trie (packed c-trie) which stores a set of strings of total length in bi…
Fully-Online Suffix Tree and Directed Acyclic Word Graph Construction for Multiple Texts
Takuya Takagi, Shunsuke Inenaga, Hiroki Arimura +2
We consider construction of the suffix tree and the directed acyclic word graph (DAWG) indexing data structures for a collection of texts, where a new symbol may be a…