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Related papers: Upper Tail Estimates with Combinatorial Proofs

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Let $\Psi_1,\Psi_2,...$ be a sequence of i.i.d. random Lipschitz functions on a complete separable metric space with unbounded metric $d$ and forward iterations $X_n$. Suppose that $X_n$ has a stationary distribution. We study the…

Probability · Mathematics 2015-08-28 Gerold Alsmeyer

Under the assumption that the distribution of a nonnegative random variable $X$ admits a bounded coupling with its size biased version, we prove simple and strong concentration bounds. In particular the upper tail probability is shown to…

Probability · Mathematics 2014-07-15 Richard Arratia , Peter Baxendale

Finite sample properties of random covariance-type matrices have been the subject of much research. In this paper we focus on the "lower tail" of such a matrix, and prove that it is subgaussian under a simple fourth moment assumption on the…

Probability · Mathematics 2013-12-11 Roberto Imbuzeiro Oliveira

For a martingale $(X_n)$ converging almost surely to a random variable $X$, the sequence $(X_n - X)$ is called martingale tail sum. Recently, Neininger [Random Structures Algorithms, 46 (2015), 346-361] proved a central limit theorem for…

Probability · Mathematics 2016-03-23 Henning Sulzbach

Consider an expander graph in which a $\mu$ fraction of the vertices are marked. A random walk starts at a uniform vertex and at each step continues to a random neighbor. Gillman showed in 1993 that the number of marked vertices seen in a…

Probability · Mathematics 2017-08-25 Shravas Rao , Oded Regev

Using terminologies of information geometry, we derive upper and lower bounds of the tail probability of the sample mean. Employing these bounds, we obtain upper and lower bounds of the minimum error probability of the 2nd kind of error…

Statistics Theory · Mathematics 2024-09-10 Shun Watanabe , Masahito Hayashi

We modify Talagrand's generic chaining method to obtain upper bounds for all p-th moments of the supremum of a stochastic process. These bounds lead to an estimate for the upper tail of the supremum with optimal deviation parameters. We…

Probability · Mathematics 2014-03-25 Sjoerd Dirksen

We derive upper bounds on the tail conditional expectation of binomial and Poisson random variables. Those upper bounds are subsequently employed to the problem of obtaining non-asymptotic lower bounds on the probability that the…

Probability · Mathematics 2017-12-07 Christos Pelekis

We develop a new probabilistic and geometric method to obtain several sharp results pertaining to the upper tail behavior of continuum Gibbs measures on infinite ensembles of random continuous curves, also known as line ensembles,…

Probability · Mathematics 2025-12-23 Shirshendu Ganguly , Milind Hegde

For a fixed positive integer $\;k,\;$ limit laws of linearly normalized $\;k$-th upper order statistics are well known. In this article, a comprehensive study of tail behaviours of limit laws of normalized $k$-th upper order statistics…

Probability · Mathematics 2015-12-11 Sreenivasan Ravi , Mandagere Chandrashekhar Manohar

We develop a class of exponential bounds for the probability that a martingale sequence crosses a time-dependent linear threshold. Our key insight is that it is both natural and fruitful to formulate exponential concentration inequalities…

Probability · Mathematics 2025-12-18 Steven R. Howard , Aaditya Ramdas , Jon McAuliffe , Jasjeet Sekhon

This work establishes combinatorial bounds on the Castelnuovo-Mumford regularity of edge ideals for trees and their multi-whiskered variants. For a tree \( T \), we give bounds for the Castelnuovo-Mumford regularity of \( I(T) \) in terms…

Commutative Algebra · Mathematics 2025-10-14 Ahtsham Ul Haq , Muhammad Usman Rashid , Muhammad Ishaq

We study the lower tail large deviation problem for subgraph counts in a random graph. Let $X_H$ denote the number of copies of $H$ in an Erd\H{o}s-R\'enyi random graph $\mathcal{G}(n,p)$. We are interested in estimating the lower tail…

Combinatorics · Mathematics 2019-04-12 Yufei Zhao

Despite the ubiquitous use of stochastic optimization algorithms in machine learning, the precise impact of these algorithms and their dynamics on generalization performance in realistic non-convex settings is still poorly understood. While…

Machine Learning · Statistics 2022-07-12 Liam Hodgkinson , Umut Şimşekli , Rajiv Khanna , Michael W. Mahoney

Li and Hu recently established variance-type O(1/n) bounds for the sample mean of independent random vectors under sublinear expectations. We extend their results to the exponential concentration regime. For bounded, independent R^d-valued…

Statistics Theory · Mathematics 2026-02-26 Nahom Seyoum

We consider a priori generalization bounds developed in terms of cross-validation estimates and the stability of learners. In particular, we first derive an exponential Efron-Stein type tail inequality for the concentration of a general…

Machine Learning · Statistics 2017-06-20 Karim Abou-Moustafa , Csaba Szepesvari

Generalized gamma distributions arise as limits in many settings involving random graphs, walks, trees, and branching processes. Pek\"oz, R\"ollin, and Ross (2016, arXiv:1309.4183 [math.PR]) exploited characterizing distributional fixed…

Probability · Mathematics 2022-08-08 Tobias Johnson , Erol Peköz

The upper tail problem in a random graph asks to estimate the probability that the number of copies of some fixed subgraph in an Erd\H{o}s--R\'enyi random graph exceeds its expectation by some constant factor. There has been much exciting…

Combinatorics · Mathematics 2020-10-27 Yang P. Liu , Yufei Zhao

Given a branching random walk, let $M_n$ be the minimum position of any member of the $n$th generation. We calculate $\mathbf{E}M_n$ to within O(1) and prove exponential tail bounds for $\mathbf{P}\{|M_n-\mathbf{E}M_n|>x\}$, under quite…

Probability · Mathematics 2009-07-24 Louigi Addario-Berry , Bruce Reed

With $\xi_{k}=\xi_{k}^{n,p}$ the number of copies of $K_k$ in the usual (Erd\H{o}s-R\'enyi) random graph $G(n,p)$, $p\geq n^{-2/(k-1)}$ and $\eta>0$, we show when $k>1$ $$\Pr(\xi_k> (1+\eta)\E \xi_k) < \exp [-\gO_{\eta,k}…

Probability · Mathematics 2012-11-12 Bobby DeMarco , Jeff Kahn