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Several researchers have proposed minimisation of maximum mean discrepancy (MMD) as a method to quantise probability measures, i.e., to approximate a target distribution by a representative point set. We consider sequential algorithms that…

Machine Learning · Statistics 2021-02-15 Onur Teymur , Jackson Gorham , Marina Riabiz , Chris. J. Oates

This paper is concerned with bounds on the maximum-likelihood (ML) decoding error probability of Reed-Solomon (RS) codes over additive white Gaussian noise (AWGN) channels. To resolve the difficulty caused by the dependence of the Euclidean…

Information Theory · Computer Science 2014-01-22 Qiutao Zhuang , Xiao Ma , Aleksander Kavcic

Ultra-Reliable Low Latency Communication (URLLC) is one of the distinctive features of the upcoming 5G wireless communication, going down to packet error rates (PER) of $10^{-9}$. In this paper we discuss the statistical properties of the…

Information Theory · Computer Science 2018-01-15 Patrick C. F. Eggers , Marko Angjelichinoski , Petar Popovski

Motivated by the fact that full diversity order is achieved using the "best-relay" selection technique, we consider opportunistic amplify-and-forward and decode-and-forward relaying systems. We focus on the outage probability of such a…

Information Theory · Computer Science 2016-11-15 Ashkan Kalantari , Mohammadali Mohammadi , Mehrdad Ardebilipour

We give new bounds on the reliability function of a typewriter channel with 5 inputs and crossover probability $1/2$. The lower bound is more of theoretical than practical importance; it improves very marginally the expurgated bound,…

Information Theory · Computer Science 2016-07-19 Marco Dalai , Yury Polyanskiy

In this letter, we present a closed-form approximation of the outage probability for the multi-hop amplify-and-forward (AF) relaying systems with fixed gain in Rayleigh fading channel. The approximation is derived from the outage event for…

Performance · Computer Science 2008-12-05 Jun Kyoung Lee , Janghoon Yang , Dong Ku Kim

This work invokes the notion of $f$-divergence to introduce a novel upper bound on the Bayes error rate of a general classification task. We show that the proposed bound can be computed by sampling from the output of a parameterized model.…

Machine Learning · Computer Science 2025-01-15 Mohammadreza Tavasoli Naeini , Ali Bereyhi , Morteza Noshad , Ben Liang , Alfred O. Hero

We propose a computationally-efficient soft-output detector for multiple-input multiple-output channels based on augmented channel puncturing in order to reduce tree processing complexity. The proposed detector, dubbed augmented WL detector…

Information Theory · Computer Science 2020-01-30 Mohammad M. Mansour

Many crucial problems in deep learning and statistical inference are caused by a variational gap, i.e., a difference between model evidence (log-likelihood) and evidence lower bound (ELBO). In particular, in a classical VAE setting that…

Machine Learning · Computer Science 2025-03-06 Łukasz Struski , Marcin Mazur , Paweł Batorski , Przemysław Spurek , Jacek Tabor

We derive various error exponents in the bee identification problem under two different decoding rules. Under na\"ive decoding, which decodes each bee independently of the others, we analyze a general discrete memoryless channel and a…

Information Theory · Computer Science 2020-11-20 Ran Tamir , Neri Merhav

For the basic maximum likelihood estimating function of the two parameters Weibull distribution, a simple proof on its global monotonicity is given to ensure the existence and uniqueness of its solution. The boundary of the function's…

Methodology · Statistics 2009-10-04 DeTao Mao , Wenyuan Li

Traditional machine learning methods usually minimize a simple loss function to learn a predictive model, and then use a complex performance measure to measure the prediction performance. However, minimizing a simple loss function cannot…

Machine Learning · Computer Science 2015-11-19 Ning Zhang , Prathamesh Chandrasekar

A new property which relies on the linear programming (LP) decoder, the approximate maximum-likelihood certificate (AMLC), is introduced. When using the belief propagation decoder, this property is a measure of how close the decoded…

Information Theory · Computer Science 2011-05-17 Idan Goldenberg , David Burshtein

Clustering is a pivotal challenge in unsupervised machine learning and is often investigated through the lens of mixture models. The optimal error rate for recovering cluster labels in Gaussian and sub-Gaussian mixture models involves ad…

Statistics Theory · Mathematics 2024-07-18 Maximilien Dreveton , Alperen Gözeten , Matthias Grossglauser , Patrick Thiran

This paper addresses a data detection problem for multiple-input multiple-output (MIMO) communication systems with hardware impairments. To facilitate maximum likelihood (ML) data detection without knowledge of nonlinear and unknown…

Signal Processing · Electrical Eng. & Systems 2024-12-10 Yujin Kang , Seunghyun Jeon , Junyong Shin , Yo-Seb Jeon , H. Vincent Poor

We propose an optimal low-resolution precoding technique that minimizes the symbol error probability of the users. Unlike existing approaches that rely on QPSK modulation, for the derivation of the minimum symbol error probability objective…

Information Theory · Computer Science 2021-10-07 Erico S. P. Lopes , Lukas T. N. Landau , Amine Mezghani

Approximate Symbol error rate (SER), outage probability and rate expressions are derived for receive diversity system employing optimum combining when both the desired and the interfering signals are subjected to Rician fading, for the…

Signal Processing · Electrical Eng. & Systems 2019-05-10 Muralikrishnan Srinivasan , Sheetal Kalyani

In this correspondence, we propose a tight lower bound to the outage probability of discrete-input Nakagami-m block-fading channels. The approach permits an efficient method for numerical evaluation of the bound, providing an additional…

Information Theory · Computer Science 2016-11-15 Khoa D. Nguyen , Albert Guillen i Fabregas , Lars K. Rasmussen

In automotive radar applications, multiple radars are used in all vehicles for improving the imaging quality. However this causes radar-to-radar interference from neighbouring vehicles, thus reducing the imaging quality. One metric to…

Information Theory · Computer Science 2020-10-05 Sudharsan Parthasarathy , Rakshith Jagannath

We consider the problem of estimating probability density functions based on sample data, using a finite mixture of densities from some component class. To this end, we introduce the $h$-lifted Kullback--Leibler (KL) divergence as a…

Machine Learning · Statistics 2024-12-24 Mark Chiu Chong , Hien Duy Nguyen , TrungTin Nguyen