no code implementations • 26 Sep 2013 • Kiyohito Nagano, Yoshinobu Kawahara
A number of discrete and continuous optimization problems in machine learning are related to convex minimization problems under submodular constraints.
no code implementations • NeurIPS 2010 • Kiyohito Nagano, Yoshinobu Kawahara, Satoru Iwata
In this paper, we introduce the minimum average cost criterion, and show that the theory of intersecting submodular functions can be used for clustering with submodular objective functions.
no code implementations • NeurIPS 2009 • Yoshinobu Kawahara, Kiyohito Nagano, Koji Tsuda, Jeff A. Bilmes
Several key problems in machine learning, such as feature selection and active learning, can be formulated as submodular set function maximization.