On Multiple Hypothesis Testing with Rejection Option

We study the problem of multiple hypothesis testing (HT) in view of a rejection option. That model of HT has many different applications. Errors in testing of M hypotheses regarding the source distribution with an option of rejecting all those hypotheses are considered. The source is discrete and arbitrarily varying (AVS). The tradeoffs among error probability exponents/reliabilities associated with false acceptance of rejection decision and false rejection of true distribution are investigated and the optimal decision strategies are outlined. The main result is specialized for discrete memoryless sources (DMS) and studied further. An interesting insight that the analysis implies is the phenomenon (comprehensible in terms of supervised/unsupervised learning) that in optimal discrimination within M hypothetical distributions one permits always lower error than in deciding to decline the set of hypotheses. Geometric interpretations of the optimal decision schemes are given for the current and known bounds in multi-HT for AVS's.Comment: 5 pages, 3 figures, submitted to IEEE Information Theory Workshop 201

Turbo: A Physical-Minded Approach to Generalized Autoencoders

This presentation explores the interconnection between the information bottleneck and a new framework called Turbo.We will first formulate a variational approximation of the information bottleneck and show how several existing models can be seen as particular cases. We then address the limitations of the information bottleneck in physical problems and propose the Turbo framework as a solution.Turbo is a generalized autoencoder framework that maximizes the mutual information between the input and output of the encoder and decoder.The framework allows for the interpretation and creation of diverse models, as well as the choice of encoder and decoder architecture.The application of Turbo to several problems will be demonstrated, including collider physics generation, image-to-image translation, and inverse problems in astronomy.Slava Voloshynovskiy &nbsp;received a radio engineer degree from Lviv Polytechnic Institute, Lviv, Ukraine, in 1993 and a Ph.D. degree in electrical engineering from the State University Lvivska Polytechnika, Lviv, Ukraine. Since 1999, he has been with the University of Geneva, Switzerland, where he is currently a Professor with the Department of Computer Science and head of the Stochastic Information Processing group. His research interests are in information-theoretic aspects of stochastic image modeling, digital watermarking, physical uncloneable functions and machine learning that includes generative models, digital twins and anomaly detection.&nbsp;Coffee will be served at 10:30.</p

Laplacian channel state estimation for

state dependent channel

On reversible information hiding system

Abstract—In this paper we consider the problem of reversible information hiding in the case when the attacker uses only discrete memoryless channels (DMC), the decoder knows only the class of channels, but not the DMC chosen by the attacker, the attacker knows the information-hiding strategy, probability distributions of all random variables, but not the side information. We introduce the notion of reversible information hiding E-capacity, which expresses the dependence of the information hiding rate on the error probability exponent E and the distortion levels for the information hider, for the attacker and for the host data approximation. The random coding bound for reversible information hiding E-capacity is found. We obtain the lower bound for reversibility information hiding capacity for E → 0. In particular, we have analyzed two special cases of the general problem formulation, pure reversibility and pure message communications. I