51 citations · 98 across the 8 of their papers we have counts for
8 papers
Computing NP-hard Repetitiveness Measures via MAX-SAT
Hideo Bannai, Keisuke Goto, Masakazu Ishihata +3
Repetitiveness measures reveal profound characteristics of datasets, and give rise to compressed data structures and algorithms working in compressed space. Alas, the computation o…
Derivative structure enumeration using binary decision diagram
Kohei Shinohara, Atsuto Seko, Takashi Horiyama +3
A derivative structure is a nonequivalent substitutional atomic configuration derived from a given primitive cell. The enumeration of derivative structures plays an essential role…
Enumeration of Distinct Support Vectors for Interactive Decision Making
Kentaro Kanamori, Satoshi Hara, Masakazu Ishihata +1
In conventional prediction tasks, a machine learning algorithm outputs a single best model that globally optimizes its objective function, which typically is accuracy. Therefore, u…
Algorithmic Meta-Theorems for Monotone Submodular Maximization
Masakazu Ishihata, Takanori Maehara, Tomas Rigaux
We consider a monotone submodular maximization problem whose constraint is described by a logic formula on a graph. Formally, we prove the following three `algorithmic metatheorems…
Practical Adversarial Combinatorial Bandit Algorithm via Compression of Decision Sets
Shinsaku Sakaue, Masakazu Ishihata, Shin-ichi Minato
We consider the adversarial combinatorial multi-armed bandit (CMAB) problem, whose decision set can be exponentially large with respect to the number of given arms. To avoid dealin…
Exact Computation of Influence Spread by Binary Decision Diagrams
Takanori Maehara, Hirofumi Suzuki, Masakazu Ishihata
Evaluating influence spread in social networks is a fundamental procedure to estimate the word-of-mouth effect in viral marketing. There are enormous studies about this topic; howe…
Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms
Mathieu Blondel, Masakazu Ishihata, Akinori Fujino +1
Polynomial networks and factorization machines are two recently-proposed models that can efficiently use feature interactions in classification and regression tasks. In this paper,…
Higher-Order Factorization Machines
Mathieu Blondel, Akinori Fujino, Naonori Ueda +1
Factorization machines (FMs) are a supervised learning approach that can use second-order feature combinations even when the data is very high-dimensional. Unfortunately, despite i…