7 papers
LNML-based Causal Discovery with Latent Confounders: Theory and Experiments
Zhongyi Que, Shin Matsushima, Kenji Yamanishi
Causal discovery with nonlinear mechanisms and latent confounders remains challenging. Existing methods often rely on either linear assumptions or causal sufficiency, limiting thei…
An Information Theoretic Framework for Graph Novelty Generation via Latent Mixture Modeling
Itsuki Nakagawa, Kenji Yamanishi
We propose an information-theoretic framework for graph novelty generation, which aims to generate data that are distinct from existing patterns while preserving global structural…
Normalized Maximum Likelihood Code-Length on Riemannian Data Spaces
Kota Fukuzawa, Atsushi Suzuki, Kenji Yamanishi
In recent years, with the large-scale expansion of graph data, there has been an increased focus on Riemannian manifold data spaces other than Euclidean space. In particular, the d…
Graph Community Augmentation with GMM-based Modeling in Latent Space
Shintaro Fukushima, Kenji Yamanishi
This study addresses the issue of graph generation with generative models. In particular, we are concerned with graph community augmentation problem, which refers to the problem of…
Foundation of Calculating Normalized Maximum Likelihood for Continuous Probability Models
Atsushi Suzuki, Kota Fukuzawa, Kenji Yamanishi
The normalized maximum likelihood (NML) code length is widely used as a model selection criterion based on the minimum description length principle, where the model with the shorte…
Clustering Change Sign Detection by Fusing Mixture Complexity
Kento Urano, Ryo Yuki, Kenji Yamanishi
This paper proposes an early detection method for cluster structural changes. Cluster structure refers to discrete structural characteristics, such as the number of clusters, when…
Balancing Summarization and Change Detection in Graph Streams
Shintaro Fukushima, Kenji Yamanishi
This study addresses the issue of balancing graph summarization and graph change detection. Graph summarization compresses large-scale graphs into a smaller scale. However, the que…