22 citations · 39 across the 11 of their papers we have counts for
18 papers
A Projected Gradient Method for Opinion Optimization with Limited Changes of Susceptibility to Persuasion
Naoki Marumo, Atsushi Miyauchi, Akiko Takeda +1
Many social phenomena are triggered by public opinion that is formed in the process of opinion exchange among individuals. To date, from the engineering point of view, a large body…
Convexification with bounded gap for randomly projected quadratic optimization
Terunari Fuji, Pierre-Louis Poirion, Akiko Takeda
Random projection techniques based on Johnson-Lindenstrauss lemma are used for randomly aggregating the constraints or variables of optimization problems while approximately preser…
A Gradient Method for Multilevel Optimization
Ryo Sato, Mirai Tanaka, Akiko Takeda
Although application examples of multilevel optimization have already been discussed since the 1990s, the development of solution methods was almost limited to bilevel cases due to…
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…
Primal-dual subgradient method for constrained convex optimization problems
Michael R. Metel, Akiko Takeda
This paper considers a general convex constrained problem setting where functions are not assumed to be differentiable nor Lipschitz continuous. Our motivation is in finding a simp…
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…