15 citations · 22 across the 14 of their papers we have counts for
15 papers
Distances on Finite Constraint Systems and LICQ Radii in Nonlinear Programming
Kohei Hatano
Mathematical programming formulations are routinely modified through algebraic reformulations, perturbations of constraint functions, and the addition or removal of constraints. So…
Online Combinatorial Linear Optimization via a Frank-Wolfe-based Metarounding Algorithm
Ryotaro Mitsuboshi, Kohei Hatano, Eiji Takimoto
Metarounding is an approach to convert an approximation algorithm for linear optimization over some combinatorial classes to an online linear optimization algorithm for the same cl…
Pure exploration in multi-armed bandits with low rank structure using oblivious sampler
Yaxiong Liu, Atsuyoshi Nakamura, Kohei Hatano +1
In this paper, we consider the low rank structure of the reward sequence of the pure exploration problems. Firstly, we propose the separated setting in pure exploration problem, wh…
Boosting-based Construction of BDDs for Linear Threshold Functions and Its Application to Verification of Neural Networks
Yiping Tang, Kohei Hatano, Eiji Takimoto
Understanding the characteristics of neural networks is important but difficult due to their complex structures and behaviors. Some previous work proposes to transform neural netwo…
Extended Formulations via Decision Diagrams
Yuta Kurokawa, Ryotaro Mitsuboshi, Haruki Hamasaki +3
We propose a general algorithm of constructing an extended formulation for any given set of linear constraints with integer coefficients. Our algorithm consists of two phases: firs…
Boosting as Frank-Wolfe
Ryotaro Mitsuboshi, Kohei Hatano, Eiji Takimoto
Some boosting algorithms, such as LPBoost, ERLPBoost, and C-ERLPBoost, aim to solve the soft margin optimization problem with the -norm regularization. LPBoost rapidly conv…
The cohomology of framed moduli spaces and the coordinate ring of torus fixed points of quotient singularities
Kohei Hatano
If two conical symplectic resolutions and are symplectic dual, the cohomology ring and the coordinate ring of -fixed points in $X_0…
A generalised log-determinant regularizer for online semi-definite programming and its applications
Yaxiong Liu, Ken-ichiro Moridomi, Kohei Hatano +1
We consider a variant of online semi-definite programming problem (OSDP): The decision space consists of semi-definite matrices with bounded -trace norm, which is a generalizati…
Improved algorithms for online load balancing
Yaxiong Liu, Kohei Hatano, Eiji Takimoto
We consider an online load balancing problem and its extensions in the framework of repeated games. On each round, the player chooses a distribution (task allocation) over serv…
Theory and Algorithms for Shapelet-based Multiple-Instance Learning
Daiki Suehiro, Kohei Hatano, Eiji Takimoto +3
We propose a new formulation of Multiple-Instance Learning (MIL), in which a unit of data consists of a set of instances called a bag. The goal is to find a good classifier of bags…
Multiple-Instance Learning by Boosting Infinitely Many Shapelet-based Classifiers
Daiki Suehiro, Kohei Hatano, Eiji Takimoto +3
We propose a new formulation of Multiple-Instance Learning (MIL). In typical MIL settings, a unit of data is given as a set of instances called a bag and the goal is to find a good…
Online linear optimization with the log-determinant regularizer
Ken-ichiro Moridomi, Kohei Hatano, Eiji Takimoto
We consider online linear optimization over symmetric positive semi-definite matrices, which has various applications including the online collaborative filtering. The problem is f…
Boosting the kernelized shapelets: Theory and algorithms for local features
Daiki Suehiro, Kohei Hatano, Eiji Takimoto +3
We consider binary classification problems using local features of objects. One of motivating applications is time-series classification, where features reflecting some local close…
Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling
Atsushi Shibagaki, Masayuki Karasuyama, Kohei Hatano +1
The problem of learning a sparse model is conceptually interpreted as the process of identifying active features/samples and then optimizing the model over them. Recently introduce…
Bandit Online Optimization Over the Permutahedron
Nir Ailon, Kohei Hatano, Eiji Takimoto
The permutahedron is the convex polytope with vertex set consisting of the vectors for all permutations (bijections) over . We study a band…