22 citations · 136 across the 44 of their papers we have counts for
6 papers · 1 filter
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…
Robust Densest Subgraph Discovery
Atsushi Miyauchi, Akiko Takeda
Dense subgraph discovery is an important primitive in graph mining, which has a wide variety of applications in diverse domains. In the densest subgraph problem, given an undirecte…
Robust Bayesian Model Selection for Variable Clustering with the Gaussian Graphical Model
Daniel Andrade, Akiko Takeda, Kenji Fukumizu
Variable clustering is important for explanatory analysis. However, only few dedicated methods for variable clustering with the Gaussian graphical model have been proposed. Even mo…
On -hyperparameter Learning via Bilevel Nonsmooth Optimization
Takayuki Okuno, Akiko Takeda, Akihiro Kawana +1
We propose a bilevel optimization strategy for selecting the best hyperparameter value for the nonsmooth regularizer with . The concerned bilevel optimization pr…
A refined convergence analysis of pDCA with applications to simultaneous sparse recovery and outlier detection
Tianxiang Liu, Ting Kei Pong, Akiko Takeda
We consider the problem of minimizing a difference-of-convex (DC) function, which can be written as the sum of a smooth convex function with Lipschitz gradient, a proper closed con…
BBCPOP: A Sparse Doubly Nonnegative Relaxation of Polynomial Optimization Problems with Binary, Box and Complementarity Constraints
Naoki Ito, Sunyoung Kim, Masakazu Kojima +2
The software package BBCPOP is a MATLAB implementation of a hierarchy of sparse doubly nonnegative (DNN) relaxations of a class of polynomial optimization (minimization) problems (…