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We derive exact upper and lower bounds for the cumulative distribution function (cdf) of the output of a neural network (NN) over its entire support subject to noisy (stochastic) inputs. The upper and lower bounds converge to the true cdf…

Machine Learning · Computer Science 2025-06-11 Andrey Kofnov , Daniel Kapla , Ezio Bartocci , Efstathia Bura

Error probabilities of random codes for memoryless channels are considered in this paper. In the area of communication systems, admissible error probability is very small and it is sometimes more important to discuss the relative gap…

Information Theory · Computer Science 2015-06-11 Junya Honda

This paper considers the difference of stop-loss payoffs where the underlying is a difference of two random variables. The goal is to study whether the comonotonic and countermonotonic modifications of those two random variables can be used…

Pricing of Securities · Quantitative Finance 2025-08-19 Hamza Hanbali , Jan Dhaene , Daniel Linders

This paper considers the problem of channel coding with a given (possibly suboptimal) maximum-metric decoding rule. A cost-constrained random-coding ensemble with multiple auxiliary costs is introduced, and is shown to achieve error…

Information Theory · Computer Science 2014-03-05 Jonathan Scarlett , Alfonso Martinez , Albert Guillén i Fàbregas

We derive a new upper bound on the reliability function for channel coding over discrete memoryless channels. Our bounding technique relies on two main elements: (i) adding an auxiliary genie-receiver that reveals to the original receiver a…

Information Theory · Computer Science 2022-09-05 Anelia Somekh-Baruch

This paper presents a saddlepoint approximation of the random-coding union bound of Polyanskiy et al. for i.i.d. random coding over discrete memoryless channels. The approximation is single-letter, and can thus be computed efficiently.…

Information Theory · Computer Science 2014-04-28 Jonathan Scarlett , Alfonso Martinez , Albert Guillén i Fàbregas

In this paper, we study the upper and the lower bounds on the joint source-channel coding error exponent with decoder side-information. The results in the paper are non-trivial extensions of the Csiszar's classical paper [5]. Unlike the…

Information Theory · Computer Science 2009-01-26 Cheng Chang

In this article we prove a new central limit theorem (CLT) for coupled particle filters (CPFs). CPFs are used for the sequential estimation of the difference of expectations w.r.t. filters which are in some sense close. Examples include the…

Statistics Theory · Mathematics 2026-01-14 Ajay Jasra , Fangyuan Yu

In this work, a new upper bound for average error probability of a two-user discrete memoryless (DM) multiple-access channel (MAC) is derived. This bound can be universally obtained for all discrete memoryless MACs with given input and…

Information Theory · Computer Science 2009-01-09 Ali Nazari , Achilleas Anastasopoulos , S. Sandeep Pradhan

This paper studies the third-order characteristic of nonsingular discrete memoryless channels and the Gaussian channel with a maximal-power constraint. The third-order term in our expansions employs a new quantity here called the channel…

Information Theory · Computer Science 2024-08-23 Recep Can Yavas , Victoria Kostina , Michelle Effros

A new lower bound for the average probability or error for a two-user discrete memoryless (DM) multiple-access channel (MAC) is derived. This bound has a structure very similar to the well-known sphere packing packing bound derived by…

Information Theory · Computer Science 2010-10-08 Ali Nazari , S. Sandeep Pradhan , Achilleas Anastasopoulos

In this work we derive a variant of the classic Glivenko-Cantelli Theorem, which asserts uniform convergence of the empirical Cumulative Distribution Function (CDF) to the CDF of the underlying distribution. Our variant allows for tighter…

Machine Learning · Computer Science 2017-11-07 Noga Alon , Moshe Babaioff , Yannai A. Gonczarowski , Yishay Mansour , Shay Moran , Amir Yehudayoff

We propose a numerically efficient method for evaluating the random-coding union bound with parameter $s$ on the error probability achievable in the finite-blocklength regime by a pilot-assisted transmission scheme employing Gaussian…

Information Theory · Computer Science 2023-08-21 A. Oguz Kislal , Alejandro Lancho , Giuseppe Durisi , Erik Ström

We derive a tight lower bound on equivocation (conditional entropy), or equivalently a tight upper bound on mutual information between a signal variable and channel outputs. The bound is in terms of the joint distribution of the signals and…

Information Theory · Computer Science 2019-01-14 Michal Hledík , Thomas R. Sokolowski , Gašper Tkačik

A sum of lognormal random variables (RVs) appears in many problems of science and engineering. For example, it is invloved in computing the distribution of recevied signal and interference powers for radio channels subject to lognormal…

Signal Processing · Electrical Eng. & Systems 2024-05-16 Fredrik Berggren

Transductive conformal prediction addresses the simultaneous prediction for multiple data points. Given a desired confidence level, the objective is to construct a prediction set that includes the true outcomes with the prescribed…

Machine Learning · Computer Science 2025-09-08 Arash Behboodi , Alvaro H. C. Correia , Fabio Valerio Massoli , Christos Louizos

We study the sample complexity of learning a uniform approximation of an $n$-dimensional cumulative distribution function (CDF) within an error $\epsilon > 0$, when observations are restricted to a minimal one-bit feedback. This serves as a…

Machine Learning · Computer Science 2026-05-12 Matteo Castiglioni , Anna Lunghi , Alberto Marchesi

We introduce mathematical tools and fixed point algorithms for optimal statistical max-min power control in cellular and cell-less massive MIMO systems. Unlike previous studies that rely on the use-and-then-forget (UatF) lower bound on…

Signal Processing · Electrical Eng. & Systems 2026-01-30 Renato Luis Garrido Cavalcante , Noor Ul Ain , Lorenzo Miretti , Slawomir Stanczak

In this paper, we provide three applications for $f$-divergences: (i) we introduce Sanov's upper bound on the tail probability of the sum of independent random variables based on super-modular $f$-divergence and show that our generalized…

Information Theory · Computer Science 2023-01-27 Saeed Masiha , Amin Gohari , Mohammad Hossein Yassaee

The Generalized Degrees of Freedom (GDoF) of the two user interference channel are characterized for all parameter regimes under the assumption of finite precision channel state information at the transmitters (CSIT), when a limited amount…

Information Theory · Computer Science 2019-08-05 Junge Wang , Bofeng Yuan , Lexiang Huang , Syed A. Jafar
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