arXiv subjects

Satoshi Hayakawa

Publications and source records attributed to Satoshi Hayakawa.

31 records · Page 2Linked to original sources

arXiv2023

Sampling-based Nyström Approximation and Kernel Quadrature

We analyze the Nyström approximation of a positive definite kernel associated with a probability measure. We first prove an improved error bound for the conventional Nyström approximation with i.i.d. sampling and singular-value decomposition in the continuous regime; the proof techniques are borrowed from statistical learning theory. We further introduce a refined selection of subspaces in Nyström approximation with theoretical guarantees that is applicable to non-i.i.d. landmark points. Finally, we discuss their application to convex kernel quadrature and give novel theoretical guarantees as well as numerical observations.

math.NA↗

arXiv2023

Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination

Calculation of Bayesian posteriors and model evidences typically requires numerical integration. Bayesian quadrature (BQ), a surrogate-model-based approach to numerical integration, is capable of superb sample efficiency, but its lack of parallelisation has hindered its practical applications. In this work, we propose a parallelised (batch) BQ method, employing techniques from kernel quadrature, that possesses an empirically exponential convergence rate. Additionally, just as with Nested Sampling, our method permits simultaneous inference of both posteriors and model evidence. Samples from our BQ surrogate model are re-selected to give a sparse set of samples, via a kernel recombination algorithm, requiring negligible additional time to increase the batch size. Empirically, we find that our approach significantly outperforms the sampling efficiency of both state-of-the-art BQ techniques and Nested Sampling in various real-world datasets, including lithium-ion battery analytics.

cs.LG↗

arXiv2022

Convergence analysis of approximation formulas for analytic functions via duality for potential energy minimization

We investigate the approximation formulas that were proposed by Tanaka & Sugihara (2019), in weighted Hardy spaces, which are analytic function spaces with certain asymptotic decay. Under the criterion of minimum worst error of $n$-point approximation formulas, we demonstrate that the formulas are nearly optimal. We also obtain the upper bounds of the approximation errors that coincide with the existing heuristic bounds in asymptotic order by duality theorem for the minimization problem of potential energy.

math.NA↗

arXiv2022

Positively Weighted Kernel Quadrature via Subsampling

We study kernel quadrature rules with convex weights. Our approach combines the spectral properties of the kernel with recombination results about point measures. This results in effective algorithms that construct convex quadrature rules using only access to i.i.d. samples from the underlying measure and evaluation of the kernel and that result in a small worst-case error. In addition to our theoretical results and the benefits resulting from convex weights, our experiments indicate that this construction can compete with the optimal bounds in well-known examples.

math.NA↗

arXiv2022

Hypercontractivity Meets Random Convex Hulls: Analysis of Randomized Multivariate Cubatures

Given a probability measure $μ$ on a set $\mathcal{X}$ and a vector-valued function $φ$, a common problem is to construct a discrete probability measure on $\mathcal{X}$ such that the push-forward of these two probability measures under $φ$ is the same. This construction is at the heart of numerical integration methods that run under various names such as quadrature, cubature, or recombination. A natural approach is to sample points from $μ$ until their convex hull of their image under $φ$ includes the mean of $φ$. Here we analyze the computational complexity of this approach when $φ$ exhibits a graded structure by using so-called hypercontractivity. The resulting theorem not only covers the classical cubature case of multivariate polynomials, but also integration on pathspace, as well as kernel quadrature for product measures.

math.PR↗

arXiv2021

Monte Carlo construction of cubature on Wiener space

In this paper, we investigate application of mathematical optimization to construction of a cubature formula on Wiener space, which is a weak approximation method of stochastic differential equations introduced by Lyons and Victoir (Cubature on Wiener Space, Proc. R. Soc. Lond. A 460, 169--198). After giving a brief review of the cubature theory on Wiener space, we show that a cubature formula of general dimension and degree can be obtained through a Monte Carlo sampling and linear programming. This paper also includes an extension of stochastic Tchakaloff's theorem, which technically yields the proof of our primary result.

math.PR↗

arXiv2021

Estimating the probability that a given vector is in the convex hull of a random sample

For a $d$-dimensional random vector $X$, let $p_{n, X}(θ)$ be the probability that the convex hull of $n$ independent copies of $X$ contains a given point $θ$. We provide several sharp inequalities regarding $p_{n, X}(θ)$ and $N_X(θ)$ denoting the smallest $n$ for which $p_{n, X}(θ)\ge1/2$. As a main result, we derive the totally general inequality $1/2 \le α_X(θ)N_X(θ)\le 3d + 1$, where $α_X(θ)$ (a.k.a. the Tukey depth) is the minimum probability that $X$ is in a fixed closed halfspace containing the point $θ$. We also show several applications of our general results: one is a moment-based bound on $N_X(\mathbb{E}[X])$, which is an important quantity in randomized approaches to cubature construction or measure reduction problem. Another application is the determination of the canonical convex body included in a random convex polytope given by independent copies of $X$, where our combinatorial approach allows us to generalize existing results in random matrix community significantly.

math.PR↗

arXiv2020

Monte Carlo Cubature Construction

In numerical integration, cubature methods are effective, especially when the integrands can be well-approximated by known test functions, such as polynomials. However, the construction of cubature formulas has not generally been known, and existing examples only represent the particular domains of integrands, such as hypercubes and spheres. In this study, we show that cubature formulas can be constructed for probability measures provided that we have an i.i.d. sampler from the measure and the mean values of given test functions. Moreover, the proposed method also works as a means of data compression, even if sufficient prior information of the measure is not available.

math.NA↗

arXiv2019

On the minimax optimality and superiority of deep neural network learning over sparse parameter spaces

Deep learning has been applied to various tasks in the field of machine learning and has shown superiority to other common procedures such as kernel methods. To provide a better theoretical understanding of the reasons for its success, we discuss the performance of deep learning and other methods on a nonparametric regression problem with a Gaussian noise. Whereas existing theoretical studies of deep learning have been based mainly on mathematical theories of well-known function classes such as Hölder and Besov classes, we focus on function classes with discontinuity and sparsity, which are those naturally assumed in practice. To highlight the effectiveness of deep learning, we compare deep learning with a class of linear estimators representative of a class of shallow estimators. It is shown that the minimax risk of a linear estimator on the convex hull of a target function class does not differ from that of the original target function class. This results in the suboptimality of linear methods over a simple but non-convex function class, on which deep learning can attain nearly the minimax-optimal rate. In addition to this extreme case, we consider function classes with sparse wavelet coefficients. On these function classes, deep learning also attains the minimax rate up to log factors of the sample size, and linear methods are still suboptimal if the assumed sparsity is strong. We also point out that the parameter sharing of deep neural networks can remarkably reduce the complexity of the model in our setting.

stat.ML↗

arXiv2018

Low-Latitude Aurorae during the Extreme Space Weather Events in 1859

The Carrington storm (September 1/2, 1859) is one of the largest magnetic storms ever observed and it has caused global auroral displays in low-latitude areas, together with a series of multiple magnetic storms during August 28 and September 4, 1859. In this study, we revisit contemporary auroral observation records to extract information on their elevation angle, color, and direction to investigate this stormy interval in detail. We first examine their equatorward boundary of "auroral emission with multiple colors" based on descriptions of elevation angle and color. We find that their locations were 36.5 deg ILAT on August 28/29 and 32.7 deg ILAT on September 1/2, suggesting that trapped electrons moved to, at least, L~1.55 and L~1.41, respectively. The equatorward boundary of "purely red emission" was likely located at 30.8 deg ILAT on September 1/2. If "purely red emission" was a stable auroral red arc, it would suggest that trapped protons moved to, at least, L~1.36. This reconstruction with observed auroral emission regions provides conservative estimations of magnetic storm intensities. We compare the auroral records with magnetic observations. We confirm that multiple magnetic storms occurred during this stormy interval, and that the equatorward expansion of the auroral oval is consistent with the timing of magnetic disturbances. It is possible that the August 28/29 interplanetary coronal mass ejections (ICMEs) cleared out the interplanetary medium, making the ICMEs for the Carrington storm on September 1/2 more geoeffective.

physics.space-ph↗

arXiv2018

A Great Space Weather Event in February 1730

Aims. Historical records provide evidence of extreme magnetic storms with equatorward auroral extensions before the epoch of systematic magnetic observations. One significant magnetic storm occurred on February 15, 1730. We scale this magnetic storm with auroral extension and contextualise it based on contemporary solar activity. Methods. We examined historical records in East Asia and computed the magnetic latitude (MLAT) of observational sites to scale magnetic storms. We also compared them with auroral records in Southern Europe. We examined contemporary sunspot observations to reconstruct detailed solar activity between 1729 and 1731. Results. We show 29 auroral records in East Asian historical documents and 37 sunspot observations. Conclusions. These records show that the auroral displays were visible at least down to 25.8° MLAT throughout East Asia. In comparison with contemporary European records, we show that the boundary of the auroral display closest to the equator surpassed 45.1° MLAT and possibly came down to 31.5° MLAT in its maximum phase, with considerable brightness. Contemporary sunspot records show an active phase in the first half of 1730 during the declining phase of the solar cycle. This magnetic storm was at least as intense as the magnetic storm in 1989, but less intense than the Carrington event.

astro-ph.SR↗

arXiv2018

The Great Space Weather Event during February 1872 Recorded in East Asia

The study of historical great geomagnetic storms is crucial for assessing the possible risks to the technological infrastructure of a modern society, caused by extreme space-weather events. The normal benchmark has been the great geomagnetic storm of September 1859, the so-called "Carrington Event". However, there are numerous records of another great geomagnetic storm in February 1872. This storm, about 12 years after the Carrington Event, resulted in comparable magnetic disturbances and auroral displays over large areas of the Earth. We have revisited this great geomagnetic storm in terms of the auroral and sunspot records in the historical documents from East Asia. In particular, we have surveyed the auroral records from East Asia and estimated the equatorward boundary of the auroral oval to be near 24.3 deg invariant latitude (ILAT), on the basis that the aurora was seen near the zenith at Shanghai (20 deg magnetic latitude, MLAT). These results confirm that this geomagnetic storm of February 1872 was as extreme as the Carrington Event, at least in terms of the equatorward motion of the auroral oval. Indeed, our results support the interpretation of the simultaneous auroral observations made at Bombay (10 deg MLAT). The East Asian auroral records have indicated extreme brightness, suggesting unusual precipitation of high-intensity, low-energy electrons during this geomagnetic storm. We have compared the duration of the East Asian auroral displays with magnetic observations in Bombay and found that the auroral displays occurred in the initial phase, main phase, and early recovery phase of the magnetic storm.

physics.space-ph↗

arXiv2008

2n-Dimensional Models with Topological Mass Generation

The 4-dimensional model with topological mass generation that has recently been presented by Dvali, Jackiw and Pi [G. Dvali, R. Jackiw, and S.-Y. Pi, Phys. Rev. Lett. 96, 081602 (2006), hep-th/0610228] is generalized to any even number of dimensions. As in the 4-dimensional model, the 2n-dimensional model describes a mass-generation phenomenon due to the presence of the chiral anomaly. In addition to this model, new 2n-dimensional models with topological mass generation are proposed, in which a Stueckelberg-type mass term plays a crucial role in the mass generation. The mass generation of a pseudoscalar field such as the eta-prime meson is discussed within this framework.

hep-th↗