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A conjectural expression of the asymptotic gap between the rate-distortion function of an arbitrary generalized Gaussian multiterminal source coding system and that of its centralized counterpart in the high-resolution regime is proposed.…

Information Theory · Computer Science 2019-08-19 Li Xie , Xiaolan Tu , Siyao Zhou , Jun Chen

We generalize the concept of extremal index of a stationary random sequence to the series scheme of identically distributed random variables with random series sizes tending to infinity in probability. We introduce new extremal indices…

Probability · Mathematics 2020-09-22 Alexey V. Lebedev

The transmission of a Gaussian source over a block-fading multiple antenna channel in the presence of a feedback link is considered. The feedback link is assumed to be an error and delay free link of capacity 1 bit per channel use. Under…

Information Theory · Computer Science 2016-11-17 Deniz Gunduz , Andrea Goldsmith , H. Vincent Poor

This paper gives improved R\'{e}nyi entropy power inequalities (R-EPIs). Consider a sum $S_n = \sum_{k=1}^n X_k$ of $n$ independent continuous random vectors taking values on $\mathbb{R}^d$, and let $\alpha \in [1, \infty]$. An R-EPI…

Information Theory · Computer Science 2016-07-21 Eshed Ram , Igal Sason

We introduce novel information-entropic variables -- a Point Divergence Gain (${\Omega}^{(l \rightarrow m)}_\alpha$), a Point Divergence Gain Entropy ($I_\alpha$), and a Point Divergence Gain Entropy Density ($P_\alpha$) -- which are…

Data Analysis, Statistics and Probability · Physics 2018-02-07 Renata Rychtáriková , Jan Korbel , Petr Macháček , Dalibor Štys

"Bounds on information combining" are entropic inequalities that determine how the information (entropy) of a set of random variables can change when these are combined in certain prescribed ways. Such bounds play an important role in…

Quantum Physics · Physics 2019-08-27 Christoph Hirche , David Reeb

Estimating mutual information between continuous random variables is often intractable and extremely challenging for high-dimensional data. Recent progress has leveraged neural networks to optimize variational lower bounds on mutual…

Machine Learning · Computer Science 2020-12-01 Ruizhi Liao , Daniel Moyer , Polina Golland , William M. Wells

The data processing inequality is central to information theory and motivates the study of monotonic divergences. However, it is not clear operationally we need to consider all such divergences. We establish a simple method for Pinsker…

Information Theory · Computer Science 2025-04-02 Ian George , Alice Zheng , Akshay Bansal

We introduce a new framework for quantum channel discrimination in an adversarial setting, where the tester plays against an adversary. We show that in asymmetric hypothesis testing, the optimal type-II error exponent is precisely…

Quantum Physics · Physics 2026-01-01 Kun Fang , Hamza Fawzi , Omar Fawzi

An information-spectrum approach is applied to solve the multiterminal source coding problem for correlated general sources, where sources may be nonstationary and/or nonergodic, and the distortion measure is arbitrary and may be…

Information Theory · Computer Science 2007-07-13 Shengtian Yang , Peiliang Qiu

We prove multiplicativity of maximal output $p$ norm of classical noise channels and thermal noise channels of arbitrary modes for all $p>1$ under the assumption that the input signal states are Gaussian states. As a direct consequence, we…

Quantum Physics · Physics 2009-11-11 Tohya Hiroshima

The data processing inequality (DPI) is a fundamental feature of information theory. Informally it states that you cannot increase the information content of a quantum system by acting on it with a local physical operation. When the smooth…

Quantum Physics · Physics 2012-09-05 Normand J. Beaudry , Renato Renner

In this paper, an achievable error exponent for the multiple-access channel with two independent sources is derived. For each user, the source messages are partitioned into two classes and codebooks are generated by drawing codewords from…

Information Theory · Computer Science 2018-09-06 Arezou Rezazadeh , Josep Font-Segura , Alfonso Martinez , Albert Guillén i Fàbregas

Extreme value theory provides rigorous theory and statistical tools for extrapolation in machine learning, particularly in settings where traditional methods struggle due to data scarcity in the tails. A broad range of tasks benefit from…

Machine Learning · Statistics 2026-05-05 Sebastian Engelke , Nicola Gnecco , Anne Sabourin

We prove a lower bound on the relative entropy between two finite-dimensional states in terms of their entropy difference and the dimension of the underlying space. The inequality is tight in the sense that equality can be attained for any…

Quantum Physics · Physics 2015-03-16 David Reeb , Michael M. Wolf

So-called improper complex signals have been shown to be beneficial in the single-antenna two-user Gaussian interference channel under the assumptions that all input signals are Gaussian and that we treat interference as noise (TIN). This…

Information Theory · Computer Science 2020-02-06 Christoph Hellings , Wolfgang Utschick

In this paper a novel stochastic optimization and extremum seeking algorithm is presented, one which is based on time-delayed random perturbations and step size adaptation. For the case of a one-dimensional quadratic unconstrained…

Optimization and Control · Mathematics 2024-10-29 Naum Dimitrieski , Michael Reyer , Mohamed-Ali Belabbas , Christian Ebenbauer

We study the statistics of the maximum and minimum of a set of $N$ random variables whose dynamical and statistical properties fall within the scope of infinite ergodic theory. These non-stationary yet recurrent systems are described, in…

Statistical Mechanics · Physics 2026-03-09 Talia Baravi , Eli Barkai

The corner points of the capacity region of the two-user Gaussian interference channel under strong or weak interference are determined using the notions of almost Gaussian random vectors, almost lossless addition of random vectors, and…

Information Theory · Computer Science 2017-02-22 Olivier Rioul

This paper quantifies the intuitive observation that adding noise reduces available information by means of non-linear strong data processing inequalities. Consider the random variables $W\to X\to Y$ forming a Markov chain, where $Y=X+Z$…

Information Theory · Computer Science 2017-11-21 Flavio P. Calmon , Yury Polyanskiy , Yihong Wu