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We propose a generalization of the asymptotic equipartition property to discrete sources with an ambiguous alphabet, and prove that it holds for irreducible stationary Markov sources with an arbitrary distinguishability relation. Our…

Information Theory · Computer Science 2022-07-29 Tamás Tasnádi , Péter Vrana

The muti-layer information bottleneck (IB) problem, where information is propagated (or successively refined) from layer to layer, is considered. Based on information forwarded by the preceding layer, each stage of the network is required…

Machine Learning · Statistics 2017-11-15 Qianqian Yang , Pablo Piantanida , Deniz Gündüz

In this note we study the error term R_{n,L}(x) in the generalized circle problem for a ball of volume x and a random lattice L of large dimension n. Our main result is the following functional central limit theorem: Fix an arbitrary…

Number Theory · Mathematics 2016-11-22 Andreas Strömbergsson , Anders Södergren

This note is an attempt to unconditionally prove the existence of weak one way functions (OWF). Starting from a provably intractable decision problem $L_D$ (whose existence is nonconstructively assured from the well-known discrete…

Computational Complexity · Computer Science 2023-07-19 Stefan Rass

A function of two variables F(x,y)is universal iff for every other function G(x,y) there exists functions h(x) and k(y) with G(x,y) = F(h(x),k(y)) Sierpinski showed that assuming the continuum hypothesis there exists a Borel function F(x,y)…

Logic · Mathematics 2012-04-25 Paul B. Larson , Arnold W. Miller , Juris Steprans , William A. R. Weiss

The information bottleneck (IB) method is a technique for extracting information that is relevant for predicting the target random variable from the source random variable, which is typically implemented by optimizing the IB Lagrangian that…

Machine Learning · Computer Science 2020-12-23 Ziqi Pan , Li Niu , Jianfu Zhang , Liqing Zhang

In relative entropy coding, a sender aims to design a stochastic code such that, on input $X \sim P_X$, the receiver can generate a sample $Y \sim P_{Y \mid X}$. It is a standard result that (1) this requires at least $I(X; Y)$ bits, (2)…

Information Theory · Computer Science 2026-05-05 Spencer Hill , Fady Alajaji , Tamás Linder , Gergely Flamich

We study a novel multi-terminal source coding setup motivated by the biclustering problem. Two separate encoders observe two i.i.d. sequences $X^n$ and $Y^n$, respectively. The goal is to find rate-limited encodings $f(x^n)$ and $g(z^n)$…

Information Theory · Computer Science 2021-11-29 Georg Pichler , Pablo Piantanida , Gerald Matz

Consider a tree network T, where each edge acts as an independent copy of a given channel M, and information is propagated from the root. For which T and M does the configuration obtained at level n of T typically contain significant…

Probability · Mathematics 2007-06-13 Elchanan Mossel

The context tree source is a source model in which the occurrence probability of symbols is determined from a finite past sequence, and is a broader class of sources that includes i.i.d. and Markov sources. The proposed source model in this…

Information Theory · Computer Science 2021-05-14 Koshi Shimada , Shota Saito , Toshiyasu Matsushima

We study universal compression of sequences generated by monotonic distributions. We show that for a monotonic distribution over an alphabet of size $k$, each probability parameter costs essentially $0.5 \log (n/k^3)$ bits, where $n$ is the…

Information Theory · Computer Science 2007-07-13 Gil I. Shamir

Normalization is fundamental to deep learning, but existing approaches such as BatchNorm, LayerNorm, and RMSNorm are variance-centric by enforcing zero mean and unit variance, stabilizing training without controlling how representations…

Machine Learning · Computer Science 2026-01-30 Xiandong Zou , Jia Li , Xiaotong Yuan , Pan Zhou

Telling apart the cause and effect between two random variables with purely observational data is a challenging problem that finds applications in various scientific disciplines. A key principle utilized in this task is the algorithmic…

Machine Learning · Computer Science 2025-08-15 Quang-Duy Tran , Bao Duong , Phuoc Nguyen , Thin Nguyen

We study the probability of Boolean functions with small max influence to become constant under random restrictions. Let $f$ be a Boolean function such that the variance of $f$ is $\Omega(1)$ and all its individual influences are bounded by…

Computational Complexity · Computer Science 2022-08-19 Ronen Eldan , Avi Wigderson , Pei Wu

The information bottleneck (IB) method seeks a compressed representation of data that preserves information relevant to a target variable for prediction while discarding irrelevant information from the original data. In its classical…

Information Theory · Computer Science 2026-02-23 Akira Kamatsuka , Takahiro Yoshida

Information Bottlenecks (IBs) learn representations that generalize to unseen data by information compression. However, existing IBs are practically unable to guarantee generalization in real-world scenarios due to the vacuous…

Machine Learning · Computer Science 2023-05-01 Yilin Lyu , Xin Liu , Mingyang Song , Xinyue Wang , Yaxin Peng , Tieyong Zeng , Liping Jing

Lacunary function systems of type $(f(M_nx))_{n\geq 1}$ for periodic functions $f$ and sequences of fast-growing matrices $(M_n)_{n\geq 1}$ exhibit many properties of independent random variables like satisfying the Central Limit Theorem or…

Probability · Mathematics 2014-08-12 Thomas Löbbe

Motivated by the lossy compression of an active-vision video stream, we consider the problem of finding the rate-distortion function of an arbitrarily varying source (AVS) composed of a finite number of subsources with known distributions.…

Information Theory · Computer Science 2016-09-08 Hari Palaiyanur , Cheng Chang , Anant Sahai

We present a novel formal system for proving quantitative-leakage properties of programs. Based on a theory of Quantitative Information Flow (QIF) that models information leakage as a noisy communication channel, it uses "gain-functions"…

Logic in Computer Science · Computer Science 2025-06-17 Chris Chen , Annabelle McIver , Carroll Morgan

Many proofs in discrete mathematics and theoretical computer science are based on the probabilistic method. To prove the existence of a good object, we pick a random object and show that it is bad with low probability. This method is…

Information Theory · Computer Science 2017-08-01 Pat Morin , Wolfgang Mulzer , Tommy Reddad
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