arXiv subjects

Takahiro Yoshida

Publications and source records attributed to Takahiro Yoshida.

At least 19 recordsLinked to original sources

arXiv2026

Kolmogorov--Nagumo Mean Frameworks for Conditional Entropy

This study focuses on conditional entropy frameworks based on the Kolmogorov--Nagumo (KN) mean. First, $(η, ψ)$-KN averaging (\texttt{EPKNAVG}), a KN-mean extension of the $η$-averaging (\texttt{EAVG}) framework for $(η, F)$-entropies, is introduced and proven to be equivalent to \texttt{EAVG} under suitable concavification conditions. Second, motivated by generalized $g$-vulnerability, a new framework is proposed for generalized $g$-conditional entropies. This framework captures conditional entropies beyond the scope of \texttt{EAVG}-type representations. In particular, it is shown that there exists an $α$ and a joint probability distribution $p_{X, Y}$ such that the Augustin--Csisz{\' a}r conditional entropy $H_α^{\mathrm{C}}(X|Y)$ cannot be represented by any $(η,F)$-entropy satisfying \texttt{EAVG}. In contrast, it is represented within the proposed framework. Furthermore, sufficient conditions are derived under which the proposed generalized $g$-conditional entropies satisfy the conditioning reduces entropy property and the data-processing inequality.

cs.IT↗

arXiv2026

Coarse-to-fine spatial GLMM for scalable prediction and multiscale analysis

We develop CF-GLMM, a scalable and covariance-free framework for spatial generalized linear mixed models with exponential-family responses, by extending coarse-to-fine spatial modeling (CFSM) beyond Gaussian data. CF-GLMM reformulates coarse-to-fine refinement on the deviance scale using iteratively updated working responses and non-constant working weights, while retaining the local-model aggregation structure of CFSM. Through validation-guided refinement, CF-GLMM automatically adapts spatial complexity and reduces the risk of oversmoothing caused by an insufficient number of basis functions. Monte Carlo experiments demonstrate accurate spatial prediction, efficient computation, and effective multiscale feature extraction, while an analysis of COVID-19 cases in Tokyo illustrates its practical utility. The proposed method is implemented in an R package spCF (https://cran.r-project.org/web/packages/spCF/).

stat.ME↗

arXiv2026

Scalable coarse-to-fine spatial downscaling

This study proposes coarse-to-fine downscaling (CF-DS), a scalable spatial downscaling method extending coarse-to-fine spatial modeling. Unlike conventional spatial-statistical downscaling methods such as area-to-point kriging, CF-DS does not require covariance matrix inversion or likelihood evaluation. Instead, it represents latent spatial processes through the synthesis of multi-scale local models, substantially reducing computational cost while approximately satisfying the aggregation constraint. Monte Carlo experiments show that CF-DS achieves predictive accuracy comparable to area-to-point kriging with dramatically shorter computation times, particularly for large datasets. An application to downscaling electricity consumption in the Tokyo metropolitan area further demonstrates its practical usefulness. The results suggest that CF-DS provides an efficient alternative for large-scale spatial downscaling problems. CF-DS is implemented in an R package spCF (https://cran.r-project.org/web/packages/spCF/index.html).

stat.ME↗

arXiv2026

A Generalized Information Bottleneck Method: A Decision-Theoretic Perspective

The information bottleneck (IB) method seeks a compressed representation of data that preserves information relevant to a target variable for prediction while discarding irrelevant information from the original data. In its classical formulation, the IB method employs mutual information to evaluate the compression between the original and compressed data and the utility of the representation for the target variable. In this study, we investigate a generalized IB problem, where the evaluation of utility is based on the $\mathcal{H}$-mutual information that satisfies the concave (\texttt{CV}) and averaging (\texttt{AVG}) conditions. This class of information measures admits a statistical decision-theoretic interpretation via its equivalence to the expected value of sample information. Based on this interpretation, we derive an alternating optimization algorithm to assess the tradeoff between compression and utility in the generalized IB problem.

cs.IT↗

arXiv2026

A Generalized Leakage Interpretation of Alpha-Mutual Information

This paper presents a unified interpretation of $α$-mutual information ($α$-MI) in terms of generalized $g$-leakage. Specifically, we present a novel interpretation of $α$-MI within an extended framework for quantitative information flow based on adversarial generalized decision problems. This framework employs the Kolmogorov-Nagumo mean and the $q$-logarithm to characterize adversarial gain. Furthermore, we demonstrate that, within this framework, the parameter $α$ can be interpreted as a measure of the adversary's risk aversion.

cs.IT↗

arXiv2026

Coarse-to-fine spatial modeling: A scalable, machine-learning-compatible spatial model

This study proposes coarse-to-fine spatial modeling (CFSM) as a scalable and machine learning-compatible alternative to conventional spatial process models. Unlike conventional covariance-based spatial models, CFSM represents spatial processes using a multiscale ensemble of local models. To ensure stable model training, larger-scale patterns that are easier to learn are modeled first, followed by smaller-scale patterns, with training terminated once the validation score stops improving. The training procedure, which is based on holdout validation, can be easily integrated with other machine learning algorithms, including random forests and neural networks. CFSM training is computationally efficient because it avoids explicit matrix inversion, which is a major computational bottleneck in conventional spatial Gaussian processes. Comparative Monte Carlo experiments demonstrated that the CFSM, as well as its integration with random forests, achieved superior predictive performance compared to existing models. Finally, we applied the proposed methods to an analysis of residential land prices in the Tokyo metropolitan area, Japan. The CFSM is implemented in an R package spCF (https://cran.r-project.org/web/packages/spCF/).

stat.ME↗

arXiv2025

Capturing Legal Reasoning Paths from Facts to Law in Court Judgments using Knowledge Graphs

Court judgments reveal how legal rules have been interpreted and applied to facts, providing a foundation for understanding structured legal reasoning. However, existing automated approaches for capturing legal reasoning, including large language models, often fail to identify the relevant legal context, do not accurately trace how facts relate to legal norms, and may misrepresent the layered structure of judicial reasoning. These limitations hinder the ability to capture how courts apply the law to facts in practice. In this paper, we address these challenges by constructing a legal knowledge graph from 648 Japanese administrative court decisions. Our method extracts components of legal reasoning using prompt-based large language models, normalizes references to legal provisions, and links facts, norms, and legal applications through an ontology of legal inference. The resulting graph captures the full structure of legal reasoning as it appears in real court decisions, making implicit reasoning explicit and machine-readable. We evaluate our system using expert annotated data, and find that it achieves more accurate retrieval of relevant legal provisions from facts than large language model baselines and retrieval-augmented methods.

cs.CL↗

arXiv2025

From Tracepoints to Timeliness: A Semi-Markov Framework for Predictive Runtime Analysis

Detecting and resolving violations of temporal constraints in real-time systems is both, time-consuming and resource-intensive, particularly in complex software environments. Measurement-based approaches are widely used during development, but often are unable to deliver reliable predictions with limited data. This paper presents a hybrid method for worst-case execution time estimation, combining lightweight runtime tracing with probabilistic modelling. Timestamped system events are used to construct a semi-Markov chain, where transitions represent empirically observed timing between events. Execution duration is interpreted as time-to-absorption in the semi-Markov chain, enabling worst-case execution time estimation with fewer assumptions and reduced overhead. Empirical results from real-time Linux systems indicate that the method captures both regular and extreme timing behaviours accurately, even from short observation periods. The model supports holistic, low-intrusion analysis across system layers and remains interpretable and adaptable for practical use.

cs.OS↗

arXiv2025

Alternating Optimization Approach for Computing $α$-Mutual Information and $α$-Capacity

This study presents alternating optimization (AO) algorithms for computing $α$-mutual information ($α$-MI) and $α$-capacity based on variational characterizations of $α$-MI using a reverse channel. Specifically, we derive several variational characterizations of Sibson, Arimoto, Augustin--Csisz{\' a}r, and Lapidoth--Pfister MI and introduce novel AO algorithms for computing $α$-MI and $α$-capacity; their performances for computing $α$-capacity are also compared. The comparison results show that the AO algorithm based on the Sibson MI's characterization has the fastest convergence speed.

cs.IT↗

arXiv2024

Hierarchical Narrative Analysis: Unraveling Perceptions of Generative AI

Written texts reflect an author's perspective, making the thorough analysis of literature a key research method in fields such as the humanities and social sciences. However, conventional text mining techniques like sentiment analysis and topic modeling are limited in their ability to capture the hierarchical narrative structures that reveal deeper argumentative patterns. To address this gap, we propose a method that leverages large language models (LLMs) to extract and organize these structures into a hierarchical framework. We validate this approach by analyzing public opinions on generative AI collected by Japan's Agency for Cultural Affairs, comparing the narratives of supporters and critics. Our analysis provides clearer visualization of the factors influencing divergent opinions on generative AI, offering deeper insights into the structures of agreement and disagreement.

cs.CL↗

arXiv2024

Hypergraph Change Point Detection using Adapted Cardinality-Based Gadgets: Applications in Dynamic Legal Structures

Hypergraphs provide a robust framework for modeling complex systems with higher-order interactions. However, analyzing them in dynamic settings presents significant computational challenges. To address this, we introduce a novel method that adapts the cardinality-based gadget to convert hypergraphs into strongly connected weighted directed graphs, complemented by a symmetrized combinatorial Laplacian. We demonstrate that the harmonic mean of the conductance and edge expansion of the original hypergraph can be upper-bounded by the conductance of the transformed directed graph, effectively preserving crucial cut information. Additionally, we analyze how the resulting Laplacian relates to that derived from the star expansion. Our approach was validated through change point detection experiments on both synthetic and real datasets, showing superior performance over clique and star expansions in maintaining spectral information in dynamic settings. Finally, we applied our method to analyze a dynamic legal hypergraph constructed from extensive United States court opinion data.

cs.SI↗

arXiv2024

A Variational Characterization of $H$-Mutual Information and its Application to Computing $H$-Capacity

$H$-mutual information ($H$-MI) is a wide class of information leakage measures, where $H=(η, F)$ is a pair of monotonically increasing function $η$ and a concave function $F$, which is a generalization of Shannon entropy. $H$-MI is defined as the difference between the generalized entropy $H$ and its conditional version, including Shannon mutual information (MI), Arimoto MI of order $α$, $g$-leakage, and expected value of sample information. This study presents a variational characterization of $H$-MI via statistical decision theory. Based on the characterization, we propose an alternating optimization algorithm for computing $H$-capacity.

cs.IT↗

arXiv2024

New Algorithms for Computing Sibson Capacity and Arimoto Capacity

The Sibson and Arimoto capacity, which are based on the Sibson and Arimoto mutual information (MI) of order α, respectively, are well-known generalizations of the channel capacity C. In this study, we derive novel alternating optimization algorithms for computing these capacities by providing new variational characterizations of the Sibson and Arimoto MI. Moreover, we prove that all iterative algorithms for computing these capacities are equivalent under appropriate conditions imposed on their initial distributions.

cs.IT↗

arXiv2024

A New Algorithm for Computing $α$-Capacity

The problem of computing $α$-capacity for $α>1$ is equivalent to that of computing the correct decoding exponent. Various algorithms for computing them have been proposed, such as Arimoto and Jitsumatsu--Oohama algorithm. In this study, we propose a novel alternating optimization algorithm for computing the $α$-capacity for $α>1$ based on a variational characterization of the Augustin--Csisz{á}r mutual information. A comparison of the convergence performance of these algorithms is demonstrated through numerical examples.

cs.IT↗

arXiv2023

Supersymmetric Baryogenesis in a Hybrid Inflation Model

We study baryogenesis in a hybrid inflation model which is embedded to the minimal supersymmetric model with right-handed neutrinos. Inflation is induced by a linear combination of the right-handed sneutrinos and its decay reheats the universe. The decay products are stored in conserved numbers, which are transported under the interactions in equilibrium as the temperature drops down. We find that at least a few percent of the initial lepton asymmetry is left under the strong wash-out due to the lighter right-handed (s)neutrinos. To account for the observed baryon number and the active neutrino masses after a successful inflation, the inflaton mass and the Majorana mass scale should be $10^{13}\,{\rm GeV}$ and ${\cal O}(10^{9}$-$10^{10})\,{\rm GeV}$, respectively.

hep-ph↗

arXiv2023

Gaussian Hierarchical Latent Dirichlet Allocation: Bringing Polysemy Back

Topic models are widely used to discover the latent representation of a set of documents. The two canonical models are latent Dirichlet allocation, and Gaussian latent Dirichlet allocation, where the former uses multinomial distributions over words, and the latter uses multivariate Gaussian distributions over pre-trained word embedding vectors as the latent topic representations, respectively. Compared with latent Dirichlet allocation, Gaussian latent Dirichlet allocation is limited in the sense that it does not capture the polysemy of a word such as ``bank.'' In this paper, we show that Gaussian latent Dirichlet allocation could recover the ability to capture polysemy by introducing a hierarchical structure in the set of topics that the model can use to represent a given document. Our Gaussian hierarchical latent Dirichlet allocation significantly improves polysemy detection compared with Gaussian-based models and provides more parsimonious topic representations compared with hierarchical latent Dirichlet allocation. Our extensive quantitative experiments show that our model also achieves better topic coherence and held-out document predictive accuracy over a wide range of corpus and word embedding vectors.

stat.ML↗

arXiv2023

A linearization for stable and fast geographically weighted Poisson regression

Although geographically weighted Poisson regression (GWPR) is a popular regression for spatially indexed count data, its development is relatively limited compared to that found for linear geographically weighted regression (GWR), where many extensions (e.g., multiscale GWR, scalable GWR) have been proposed. The weak development of GWPR can be attributed to the computational cost and identification problem in the underpinning Poisson regression model. This study proposes linearized GWPR (L-GWPR) by introducing a log-linear approximation into the GWPR model to overcome these bottlenecks. Because the L-GWPR model is identical to the Gaussian GWR model, it is free from the identification problem, easily implemented, computationally efficient, and offers similar potential for extension. Specifically, L-GWPR does not require a double-loop algorithm, which makes GWPR slow for large samples. Furthermore, we extended L-GWPR by introducing ridge regularization to enhance its stability (regularized L-GWPR). The results of the Monte Carlo experiments confirmed that regularized L-GWPR estimates local coefficients accurately and computationally efficiently. Finally, we compared GWPR and regularized L-GWPR through a crime analysis in Tokyo.

stat.ME↗

arXiv2022

Subcritical regime of hybrid inflation with modular $A_4$ symmetry

We consider a supergravity model that has the modular $A_4$ symmetry and discuss the interplay between the neutrino mixing and inflation. The model contains right-handed neutrinos that have the Majorana masses and additional Yukawa couplings to the waterfall field. In the model an active neutrino is massless and we find that only the inverted hierarchy is allowed and the Majorana phase is predicted to be around $\pm (120\text{--}180)^\circ$ from the observed neutrino mixing data. In the early universe, one of right-handed sneutrinos plays the role of the inflaton field. Focusing on the subcritical regime of the hybrid inflation that is consistent with the cosmic microwave background data, we analyze the dynamics of the scalar sector and derive an upper bound $\mathcal{O}(10^{10})~{\rm GeV}$ on the scale of the Majorana mass.

hep-ph↗