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Empirical risk minimization over classes functions that are bounded for some version of the variation norm has a long history, starting with Total Variation Denoising (Rudin et al., 1992), and has been considered by several recent articles,…

Statistics Theory · Mathematics 2019-08-26 Aurélien F. Bibaut , Mark J. van der Laan

We consider the problem of minimizing the sum of a Lipschitz differentiable convex function $f$ and a proper closed convex function $h$ that admits efficient linear minimization oracles, subject to multiple smooth convex inequality…

Optimization and Control · Mathematics 2026-05-22 Xiaozhou Wang , Ting Kei Pong , Zev Woodstock

Inspired by a paper of Erik Westzynthius,we build on work of Harlan Stevens and Hans-Joachim Kanold. Let $k \gt 2$ be the number of distinct prime divisors of a positive integer $n$. In 1977, Stevens used Bonferroni inequalities to get an…

Number Theory · Mathematics 2014-03-28 Gerhard R. Paseman

This paper is concerned with nonparametric estimation of the L\'evy density of a pure jump L\'evy process. The sample path is observed at $n$ discrete instants with fixed sampling interval. We construct a collection of estimators obtained…

Statistics Theory · Mathematics 2010-10-01 Fabienne Comte , Valentine Genon-Catalot

In this paper, we consider the minimization of a nonsmooth nonconvex objective function $f(x)$ over a closed convex subset $\mathcal{X}$ of $\mathbb{R}^n$, with additional nonsmooth nonconvex constraints $c(x) = 0$. We develop a unified…

Optimization and Control · Mathematics 2024-04-16 Nachuan Xiao , Kuangyu Ding , Xiaoyin Hu , Kim-Chuan Toh

We consider the problem of heteroscedastic linear regression, where, given $n$ samples $(\mathbf{x}_i, y_i)$ from $y_i = \langle \mathbf{w}^{*}, \mathbf{x}_i \rangle + \epsilon_i \cdot \langle \mathbf{f}^{*}, \mathbf{x}_i \rangle$ with…

Machine Learning · Statistics 2023-07-04 Dheeraj Baby , Aniket Das , Dheeraj Nagaraj , Praneeth Netrapalli

We find two-sides estimates for the best uniform approximations of classes of convolutions of $2\pi$-periodic functions from unit ball of the space $L_p, 1 \le p <\infty,$ with fixed kernels, modules of Fourier coefficients of which satisfy…

Classical Analysis and ODEs · Mathematics 2020-08-05 A. S. Serdyuk , I. V. Sokolenko

Consider averages along the prime integers $ \mathbb P $ given by \begin{equation*} \mathcal{A}_N f (x) = N ^{-1} \sum_{ p \in \mathbb P \;:\; p\leq N} (\log p) f (x-p). \end{equation*} These averages satisfy a uniform scale-free $ \ell…

Classical Analysis and ODEs · Mathematics 2020-06-23 Rui Han , Ben Krause , Michael Lacey , Fan Yang

Let $\tau$ denote the divisor function, and $f$ be any multiplicative function that satisfies some mild hypotheses. We establish the asymptotic formula or non-trivial upper bound for the shifted convolution sum $\sum_{n \leq…

Number Theory · Mathematics 2022-04-19 Yujiao Jiang , Guangshi Lü

We study subsampling estimators for the limit variance \[ \sigma^2=Var(X_1)+2 \sum_{k=2}^\infty Cov(X_1,X_k) \] of partial sums of a stationary stochastic process $(X_k)_{k\geq 1}$. We establish $L_2$-consistency of a non-overlapping block…

Statistics Theory · Mathematics 2015-06-09 Herold Dehling , Roland Fried , Olimjon Sh. Sharipov , Daniel Vogel , Max Wornowizki

We introduce a numerical method for the approximation of functions which are analytic on compact intervals, except at the endpoints. This method is based on variable transforms using particular parametrized exponential and…

Numerical Analysis · Mathematics 2016-09-06 Ben Adcock , Jésus Martín-Vaquero , Mark Richardson

Let $\lambda_{\phi}(n)$ be the Fourier coefficients of a Hecke holomorphic or Hecke--Maass cusp form on ${\rm SL}_2(\mathbb Z)$, and $f$ be any multiplicative function that satisfies two mild hypotheses. We establish a non-trivial upper…

Number Theory · Mathematics 2022-04-19 Yujiao Jiang , Guangshi Lü

Function values are, in some sense, "almost as good" as general linear information for $L_2$-approximation (optimal recovery, data assimilation) of functions from a reproducing kernel Hilbert space. This was recently proved by new upper…

Numerical Analysis · Mathematics 2022-03-23 Aicke Hinrichs , David Krieg , Erich Novak , Jan Vybiral

In this paper, we will generate a convex iterative FP thresholding algorithm to solve the problem $(FP^{\lambda}_{a})$. Two schemes of convex iterative FP thresholding algorithms are generated. One is convex iterative FP thresholding…

Optimization and Control · Mathematics 2019-05-29 Angang Cui , Jigen Peng , Haiyang Li , Meng Wen

Fast Incremental Expectation Maximization (FIEM) is a version of the EM framework for large datasets. In this paper, we first recast FIEM and other incremental EM type algorithms in the {\em Stochastic Approximation within EM} framework.…

Machine Learning · Computer Science 2021-01-01 Gersende Fort , P. Gach , E. Moulines

Avikainen showed that, for any $p,q \in [1,\infty)$, and any function $f$ of bounded variation in $\mathbb{R}$, it holds that $\mathbb{E}[|f(X)-f(\widehat{X})|^{q}] \leq C(p,q) \mathbb{E}[|X-\widehat{X}|^{p}]^{\frac{1}{p+1}}$, where $X$ is…

Probability · Mathematics 2020-11-30 Dai Taguchi , Akihiro Tanaka , Tomooki Yuasa

Let $\Omega \subset \mathbb{R}^n$ be a convex domain and let $f:\Omega \rightarrow \mathbb{R}$ be a positive, subharmonic function (i.e. $\Delta f \geq 0$). Then $$ \frac{1}{|\Omega|} \int_{\Omega}{f dx} \leq \frac{c_n}{ |\partial \Omega| }…

A Hardy-Littlewood integral inequality on finite intervals with a concave weight is established. Given a function f on an interval [a,b], it is shown that the square of the weighted L^2 norm of its derivative f' is bounded by the product of…

Classical Analysis and ODEs · Mathematics 2015-07-06 Horst Alzer , Man Kam Kwong

We study a family of sparse estimators defined as minimizers of some empirical Lipschitz loss function -- which include the hinge loss, the logistic loss and the quantile regression loss -- with a convex, sparse or group-sparse…

Machine Learning · Statistics 2021-09-23 Antoine Dedieu

Error bounds, which refer to inequalities that bound the distance of vectors in a test set to a given set by a residual function, have proven to be extremely useful in analyzing the convergence rates of a host of iterative methods for…

Optimization and Control · Mathematics 2015-12-14 Zirui Zhou , Anthony Man-Cho So