49 citations · 118 across the 8 of their papers we have counts for
14 papers
Invariance-adapted decomposition and Lasso-type contrastive learning
Masanori Koyama, Takeru Miyato, Kenji Fukumizu
Recent years have witnessed the effectiveness of contrastive learning in obtaining the representation of dataset that is useful in interpretation and downstream tasks. However, the…
Unsupervised Learning of Equivariant Structure from Sequences
Takeru Miyato, Masanori Koyama, Kenji Fukumizu
In this study, we present meta-sequential prediction (MSP), an unsupervised framework to learn the symmetry from the time sequence of length at least three. Our method leverages th…
Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective
Ruixiang Zhang, Masanori Koyama, Katsuhiko Ishiguro
Learning controllable and generalizable representation of multivariate data with desired structural properties remains a fundamental problem in machine learning. In this paper, we…
Meta Learning as Bayes Risk Minimization
Shin-ichi Maeda, Toshiki Nakanishi, Masanori Koyama
Meta-Learning is a family of methods that use a set of interrelated tasks to learn a model that can quickly learn a new query task from a possibly small contextual dataset. In this…
Reconnaissance and Planning algorithm for constrained MDP
Shin-ichi Maeda, Hayato Watahiki, Shintarou Okada +1
Practical reinforcement learning problems are often formulated as constrained Markov decision process (CMDP) problems, in which the agent has to maximize the expected return while…
Optuna: A Next-generation Hyperparameter Optimization Framework
Takuya Akiba, Shotaro Sano, Toshihiko Yanase +2
The purpose of this study is to introduce new design-criteria for next-generation hyperparameter optimization software. The criteria we propose include (1) define-by-run API that a…