22 citations · 136 across the 44 of their papers we have counts for
5 papers · 1 filter
BODAME: Bilevel Optimization for Defense Against Model Extraction
Yuto Mori, Atsushi Nitanda, Akiko Takeda
Model extraction attacks have become serious issues for service providers using machine learning. We consider an adversarial setting to prevent model extraction under the assumptio…
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…
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…
A Unified Robust Classification Model
Akiko Takeda, Hiroyuki Mitsugi, Takafumi Kanamori
A wide variety of machine learning algorithms such as support vector machine (SVM), minimax probability machine (MPM), and Fisher discriminant analysis (FDA), exist for binary clas…