28 citations · 94 across the 27 of their papers we have counts for
16 papers · 1 filter
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…
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…
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…
Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization
Léo Andeol, Yusei Kawakami, Yuichiro Wada +3
Domain shifts in the training data are common in practical applications of machine learning; they occur for instance when the data is coming from different sources. Ideally, a ML m…
Robust modal regression with direct log-density derivative estimation
Hiroaki Sasaki, Tomoya Sakai, Takafumi Kanamori
Modal regression is aimed at estimating the global mode (i.e., global maximum) of the conditional density function of the output variable given input variables, and has led to regr…
Estimating Density Models with Truncation Boundaries using Score Matching
Song Liu, Takafumi Kanamori, Daniel J. Williams
Truncated densities are probability density functions defined on truncated domains. They share the same parametric form with their non-truncated counterparts up to a normalizing co…