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We characterize the best $L_{2}$ approximation to a multivariate function by linear combinations of ridge functions multiplied by some fixed weight functions. In the special case when the weight functions are constants, we propose explicit…

Classical Analysis and ODEs · Mathematics 2007-08-27 Vugar Ismailov

In this paper, we define finitely additive, probability and modular functions over semiring-like structures. We investigate finitely additive functions with the help of complemented elements of a semiring. We also generalize some classical…

Commutative Algebra · Mathematics 2018-01-26 Peyman Nasehpour , Amir Hossein Parvardi

We prove an exact relationship between the optimal denoising function and the data distribution in the case of additive Gaussian noise, showing that denoising implicitly models the structure of data allowing it to be exploited in the…

Neural and Evolutionary Computing · Computer Science 2017-09-11 Heikki Arponen , Matti Herranen , Harri Valpola

We describe polynomials of the best uniform approximation to sgn(x) on the union of two intervals in terms of special conformal mappings. This permits us to find the exact asymptotic behavior of the error of this approximation.

Complex Variables · Mathematics 2012-02-07 Alexandre Eremenko , Peter Yuditskii

This paper considers the problem of smoothing convex functions and sets, seeking the nearest smooth convex function or set to a given one. For convex cones and sublinear functions, a full characterization of the set of all optimal…

Optimization and Control · Mathematics 2025-08-22 Thabo Samakhoana , Benjamin Grimmer

Bayesian optimization is a coherent, ubiquitous approach to decision-making under uncertainty, with applications including multi-arm bandits, active learning, and black-box optimization. Bayesian optimization selects decisions (i.e.…

Machine Learning · Computer Science 2023-12-13 Samuel Stanton , Wesley Maddox , Andrew Gordon Wilson

Probabilities in the multiverse can be calculated by assuming that we are typical representatives in a given reference class. But is this class well defined? What should be included in the ensemble in which we are supposed to be typical?…

High Energy Physics - Theory · Physics 2008-11-26 Jaume Garriga , Alexander Vilenkin

Factor score predictors are to be computed when the individual scores on the factors are of interest. Conditions for a perfect inter-correlation of the regression/best linear factor score predictor, the best linear conditionally unbiased…

Applications · Statistics 2015-11-03 André Beauducel

We introduce the boolean convolution for probability measures on the unit circle. Roughly speaking, it describes the distribution of the product of two boolean independent unitary random variables. We find an analogue of the characteristic…

Functional Analysis · Mathematics 2009-06-13 Uwe Franz

A copula of continuous random variables $X$ and $Y$ is called an \emph{implicit dependence copula} if there exist functions $\alpha$ and $\beta$ such that $\alpha(X) = \beta(Y)$ almost surely, which is equivalent to $C$ being factorizable…

Statistics Theory · Mathematics 2016-06-29 Songkiat Sumetkijakan

Gaussian process modeling is a standard tool for building emulators for computer experiments, which are usually used to study deterministic functions, for example, a solution to a given system of partial differential equations. This work…

Statistics Theory · Mathematics 2021-10-01 Wenjia Wang

Conformal prediction is a learning framework controlling prediction coverage of prediction sets, which can be built on any learning algorithm for point prediction. This work proposes a learning framework named conformal loss-controlling…

Machine Learning · Computer Science 2024-01-24 Di Wang , Ping Wang , Zhong Ji , Xiaojun Yang , Hongyue Li

We consider nonparametric regression with functional covariates, that is, they are elements of an infinite-dimensional Hilbert space. A locally polynomial estimator is constructed, where an orthonormal basis and various tuning parameters…

Statistics Theory · Mathematics 2025-04-09 Moritz Jirak , Alois Kneip , Alexander Meister , Mario Pahl

A Bayesian net (BN) is more than a succinct way to encode a probabilistic distribution; it also corresponds to a function used to answer queries. A BN can therefore be evaluated by the accuracy of the answers it returns. Many algorithms for…

Artificial Intelligence · Computer Science 2013-02-08 Russell Greiner , Adam J. Grove , Dale Schuurmans

We introduce an empirical functional $\Psi$ that is an optimal uniform mean estimator: Let $F\subset L_2(\mu)$ be a class of mean zero functions, $u$ is a real valued function, and $X_1,\dots,X_N$ are independent, distributed according to…

Probability · Mathematics 2026-03-06 Daniel Bartl , Shahar Mendelson

For a polynomial f: {-1, 1}^n --> C, we define the partition function as the average of e^{lambda f(x)} over all points x in {-1, 1}^n, where lambda in C is a parameter. We present a quasi-polynomial algorithm, which, given such f, lambda…

Data Structures and Algorithms · Computer Science 2016-11-30 Alexander Barvinok

We show examples of total Boolean functions that depend on $n$ variables and have spectral sensitivity $\Theta(\sqrt{\log n})$, which is asymptotically minimal. Our main new function combines the Hamming code with the Boolean address…

Computational Complexity · Computer Science 2025-02-21 Krišjānis Prūsis , Jevgēnijs Vihrovs

It is increasingly common to encounter time-varying random fields on networks (metabolic networks, sensor arrays, distributed computing, etc.). This paper considers the problem of optimal, nonlinear prediction of these fields, showing from…

Probability · Mathematics 2022-02-17 Cosma Rohilla Shalizi

We discuss quantum algorithms, based on the Bernstein-Vazirani algorithm, for finding which variables a Boolean function depends on. There are 2^n possible linear Boolean functions of n variables; given a linear Boolean function, the…

Quantum Physics · Physics 2010-06-09 Dominik F. Floess , Erika Andersson , Mark Hillery

Bayesian optimization is proposed for automatic learning of optimal controller parameters from experimental data. A probabilistic description (a Gaussian process) is used to model the unknown function from controller parameters to a…

Systems and Control · Computer Science 2019-01-24 Matthias Neumann-Brosig , Alonso Marco , Dieter Schwarzmann , Sebastian Trimpe