4.3k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- The University of TokyoJP154 papers
- National Astronomical Observatory of JapanJP111 papers
- The Graduate University for Advanced Studies, SOKENDAIJP101 papers
- Kavli Institute for the Physics and Mathematics of the UniverseJP67 papers
- Nagoya UniversityJP57 papers
- Hiroshima UniversityJP47 papers
- Centre National de la Recherche ScientifiqueFR45 papers
- Tohoku UniversityJP42 papers
- University College LondonGB42 papers
- Institute of Astronomy and Astrophysics, Academia SinicaTW41 papers
- Korea Astronomy and Space Science InstituteKR41 papers
- Tokyo Institute of TechnologyJP41 papers
15 papers · 1 filter
Nonstationary ETAS models for nonstandard earthquakes
Takao Kumazawa, Yosihiko Ogata
The conditional intensity function of a point process is a useful tool for generating probability forecasts of earthquakes. The epidemic-type aftershock sequence (ETAS) model is de…
On Conditional Prediction Errors in Mixed Models with Application to Small Area Estimation
Shonosuke Sugasawa, Tatsuya Kubokawa
The empirical Bayes estimators in mixed models are useful for small area estimation in the sense of increasing precision of prediction for small area means, and one wants to know t…
Macroscopic quantum information processing using spin coherent states
Tim Byrnes, Daniel Rosseau, Megha Khosla +6
Previously a new scheme of quantum information processing based on spin coherent states of two component Bose-Einstein condensates was proposed (Byrnes {\it et al.} Phys. Rev. A 85…
On the spike train variability characterized by variance-to-mean power relationship
Shinsuke Koyama
We propose a statistical method for modeling the non-Poisson variability of spike trains observed in a wide range of brain regions. Central to our approach is the assumption that t…
Fluctuation scaling in neural spike trains
Shinsuke Koyama, Ryota Kobayashi
Fluctuation scaling has been observed universally in a wide variety of phenomena. In time series that describe sequences of events, fluctuation scaling is expressed as power functi…
Numerical studies of the optimization of the first eigenvalue of the heat diffusion in inhomogeneous media
Kaname Matsue, Hisashi Naito
In this paper, we study optimization of the first eigenvalue of the heat equation with spatially nonuniform conductivity on a bounded domain under several constraints for the condu…
Quadratic covariation estimation of an irregularly observed semimartingale with jumps and noise
Yuta Koike
This paper presents a central limit theorem for a pre-averaged version of the realized covariance estimator for the quadratic covariation of a discretely observed semimartingale wi…
Support Consistency of Direct Sparse-Change Learning in Markov Networks
Song Liu, Taiji Suzuki, Raissa Relator +3
We study the problem of learning sparse structure changes between two Markov networks and . Rather than fitting two Markov networks separately to two sets of data and figuri…
Kernel Mean Shrinkage Estimators
Krikamol Muandet, Bharath Sriperumbudur, Kenji Fukumizu +2
A mean function in a reproducing kernel Hilbert space (RKHS), or a kernel mean, is central to kernel methods in that it is used by many classical algorithms such as kernel principa…
Time endogeneity and an optimal weight function in pre-averaging covariance estimation
Yuta Koike
We establish a central limit theorem for a class of pre-averaging covariance estimators in a general endogenous time setting. In particular, we show that the time endogeneity has n…
Characteristic Kernels and Infinitely Divisible Distributions
Yu Nishiyama, Kenji Fukumizu
We connect shift-invariant characteristic kernels to infinitely divisible distributions on . Characteristic kernels play an important role in machine learning appli…
MIMO Zero-Forcing Performance Evaluation Using the Holonomic Gradient Method
Constantin Siriteanu, Akimichi Takemura, Satoshi Kuriki +2
For multiple-input multiple-output (MIMO) spatial-multiplexing transmission, zero-forcing detection (ZF) is appealing because of its low complexity. Our recent MIMO ZF performance…
Sparse principal component regression with adaptive loading
Shuichi Kawano, Hironori Fujisawa, Toyoyuki Takada +1
Principal component regression (PCR) is a two-stage procedure that selects some principal components and then constructs a regression model regarding them as new explanatory variab…
Phase transitions and sample complexity in Bayes-optimal matrix factorization
Yoshiyuki Kabashima, Florent Krzakala, Marc Mézard +2
We analyse the matrix factorization problem. Given a noisy measurement of a product of two matrices, the problem is to estimate back the original matrices. It arises in many applic…
Schur Complement Based Analysis of MIMO Zero-Forcing for Rician Fading
Constantin Siriteanu, Akimichi Takemura, Satoshi Kuriki +2
For multiple-input/multiple-output (MIMO) spatial multiplexing with zero-forcing detection (ZF), signal-to-noise ratio (SNR) analysis for Rician fading involves the cumbersome nonc…