Michael Christoph Gastpar

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Attention-based transformers have achieved tremendous success across a variety of disciplines including natural languages. To deepen our understanding of their sequential modeling capabilities, there is a growing interest in using Markov input processes to ...

We give an information-theoretic interpretation of Canonical Correlation Analysis (CCA) via (relaxed) Wyner's common information. CCA permits to extract from two high-dimensional data sets low-dimensional descriptions (features) that capture the commonalit ...

Function computation of arbitrarily correlated discrete sources over Gaussian networks with multiple access components but no broadcast is studied. Two classes of functions are considered: the arithmetic sum function and the frequency histogram function. T ...

Based on the recent compute-and-forward technique [1], a novel communication strategy is proposed under which functions of the channel state information are forwarded along the network. Those functions are chosen such that on the one hand, they can be effi ...

There has been growing interest in studying connections between generalization error of learning algorithms and information measures. In this work, we generalize a result that employs the maximal leakage, a measure of leakage of information, and explore ho ...

What makes a source–channel communication system optimal? It is shown that in order to achieve an optimal cost–distortion tradeoff, the source and the channel have to be matched in a probabilistic sense. The match (or lack of it) involves the source distribution, the distortion measure, the channel conditional distribution, and the channel input cost func- tion. Closed-form necessary and sufficient expressions relating the above entities are given. This generalizes both the separa- tion-based approach as we

A coding scheme for the two-receivers Gaussian broadcast channel (BC) with feedback is proposed. For some asymmetric settings it achieves new rate pairs. Moreover, it achieves prelog 2 when the noises at the two receivers are fully positively correlated and of unequal variances, thus allowing us to complete the characterization of the prelog of the two-receivers Gaussian BC with feedback. The new achievable rates also allow us to determine the asymptotic power offset when the noises at the two receivers are

Browse Conference Publications > Information Theory and Applic ... Back to Results | Next » Help Random access with physical-layer network coding This paper appears in: Information Theory and Applications Workshop (ITA), 2013 Date of Conference: 10-15 Feb. 2013 Author(s): Goseling, Jasper Stochastic Operations Research, University of Twente, The Netherlands Gastpar, Michael; Weber, Jos H. Page(s): 1 - 7 Product Type: Conference Publications Access Full Text in PDF Download Citations Email Print Rights And P

Recovery of the sparsity pattern (or support) of an unknown sparse vector from a small number of noisy linear mea- surements is an important problem in compressed sensing. In this paper, the high-dimensional setting is considered. It is shown that if the m ...

Wyner's common information is a measure that quantifies and assesses the commonality between two random variables. Based on this, we introduce a novel two-step procedure to construct features from data, referred to as Common Information Components Analysis ...

The successful design of sensor network architectures depends crucially on the structure of the sampling, observation, and communication processes. One of the most fundamental questions concerns the sufficiency of discrete approximations in time, space, an ...

This paper considers the problem of distributed lossy compression where the goal is to recover one or more linear combinations of the sources at the decoder, subject to distortion constraints. For certain configurations, it is known that codes with algebra ...

The usefulness of lattice codes is investigated for two-user Gaussian interference channels (IC). A coding scheme based on the compute-and-forward technique is shown to achieve the capacity region of the Gaussian IC under strong interference. The proposed ...

The capacity scaling laws of two overlaid networks sharing the same wireless resources with different priorities are investigated. The primary network is assumed to operate in an order-optimal fashion to achieve its standalone capacity scaling law. The sec ...

Caching is a technique that alleviates networks during peak hours by transmitting partial information before a request for any is made. In a lossy setting of Gaussian databases, we study a single-user model in which good caching strategies minimize the dat ...

One major challenge in distributed learning is to efficiently learn for each client when the data across clients is heterogeneous or non iid (not independent or identically distributed). This provides a significant challenge as the data of the other client ...

The single-server multi-message private information retrieval with side information problem is studied for general cases. In this problem, K independent messages are stored at a single server. One user initially has M messages as side information and wants ...

Inspired by Sibson’s alpha-mutual information, we introduce a new parametric class of universal predictors. This class interpolates two well-known predictors, the mixture estimator, that includes the Laplace and the Krichevsky-Trofimov predictors, and the ...

The capacity of a wireless lattice network with local interference is studied. In particular, the transport capacity under a multiple unicast traffic pattern is studied for a network of nodes placed on a hexagonal lattice. Different ways of handling broadcast and superposition lead to various communication models. In particular we consider models arising from the use of compute-and-forward/physical layer network coding. If neither broadcast nor superposition is exploited, the normalized transport capacity w

Michael Christoph Gastpar

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Privacy in Retrieval, Computing, and Learning

Attention with Markov: A Curious Case of Single-layer Transformers

Common Information Components Analysis

Computation over Gaussian networks with orthogonal components

Functional Forwarding of Channel State Information

Learning and Adaptive Data Analysis via Maximal Leakage

To code or not to code: lossy source-channel communication revisited

New achievable rates for the Gaussian broadcast channel with feedback

Random Access with Physical-layer Network Coding

Approximate Sparsity Pattern Recovery: Information-Theoretic Lower Bounds

Common Information Components Analysis

Sensing reality and communicating bits: A dangerous liaison

Distributed Lossy Computation with Structured Codes: From Discrete to Continuous Sources

On lattice codes for Gaussian interference channels

Capacity Scaling of Cognitive Networks: Beyond Interference-Limited Communication

Caching (Bivariate) Gaussians

Personalized Privacy-Preserving Distributed Learning on Heterogeneous Data

Single-server Multi-message Private Information Retrieval with Side Information: the General Cases

Alpha-NML Universal Predictors

Compute-and-forward on wireless lattice networks with local interference