28 citations · 94 across the 27 of their papers we have counts for
27 papers
Bootstrap Error Estimation and Sketch-Size Selection for Sketched Ridge Regression
Akito Narahara, Takayuki Kawashima, Takafumi Kanamori
Randomized sketching reduces the computational cost of large ridge-regression problems, but the coefficient error depends on the realized sketch. We extend the paired-row bootstrap…
Identifiability and Estimation for Unlabeled Finite Mixtures under Marginal Independence
Takafumi Kanamori, Yushi Hirose, Shohei Yamamoto
We study component recovery and mixing-matrix estimation from unlabeled finite mixtures whose observable distributions share the same latent components but have unknown mixing weig…
Mixture Proportion Estimation and Weakly-supervised Kernel Test for Conditional Independence
Yushi Hirose, Akito Narahara, Takafumi Kanamori
Mixture proportion estimation (MPE) aims to estimate class priors from unlabeled data. This task is a critical component in weakly supervised learning, such as PU learning, learnin…
Denoising Cosine Similarity: A Theory-Driven Approach for Efficient Representation Learning
Takumi Nakagawa, Yutaro Sanada, Hiroki Waida +5
Representation learning has been increasing its impact on the research and practice of machine learning, since it enables to learn representations that can apply to various downstr…
Towards Understanding the Mechanism of Contrastive Learning via Similarity Structure: A Theoretical Analysis
Hiroki Waida, Yuichiro Wada, Léo Andéol +3
Contrastive learning is an efficient approach to self-supervised representation learning. Although recent studies have made progress in the theoretical understanding of contrastive…
Deep Clustering with a Constraint for Topological Invariance based on Symmetric InfoNCE
Yuhui Zhang, Yuichiro Wada, Hiroki Waida +3
We consider the scenario of deep clustering, in which the available prior knowledge is limited. In this scenario, few existing state-of-the-art deep clustering methods can perform…