Publications de Michael Christoph Gastpar

Publications associées (131)

Reduced-Dimension Linear Transform Coding of Correlated Signals in Networks

A model called the linear transform network (LTN) is proposed to analyze the compression and estimation of correlated signals transmitted over directed acyclic graphs (DAGs). An LTN is a DAG network with multiple source and receiver nodes. Source nodes tra ...

Institute of Electrical and Electronics Engineers2012

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

In this work, the probability of an event under some joint distribution is bounded by measuring it with the product of the marginals instead (which is typically easier to analyze) together with a measure of the dependence between the two random variables. ...

2021

The Gray-Wyner Network and Wyner's Common Information for Gaussian Sources

Michael Christoph Gastpar, Erixhen Sula

This paper presents explicit solutions for two related non-convex information extremization problems due to Gray and Wyner in the Gaussian case. The first problem is the Gray-Wyner network subject to a sum-rate constraint on the two private links. Here, ou ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

The Sampling Rate-Distortion Tradeoff for Sparsity Pattern Recovery in Compressed Sensing

Michael Christoph Gastpar

Recovery of the sparsity pattern (or support) of an unknown sparse vector from a limited number of noisy linear measurements is an important problem in compressed sensing. In the high-dimensional setting, it is known that recovery with a vanishing fraction ...

Institute of Electrical and Electronics Engineers2012

Compute-Forward for DMCs: Simultaneous Decoding of Multiple Combinations

Michael Christoph Gastpar, Adriano Pastore, Sung Hoon Lim, Chen Feng

Algebraic network information theory is an emerging facet of network information theory, studying the achievable rates of random code ensembles that have algebraic structure, such as random linear codes. A distinguishing feature is that linear combinations ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Remote Source Coding Under Gaussian Noise: Dueling Roles of Power and Entropy Power

Michael Christoph Gastpar

The distributed remote source coding (the so-called CEO) problem is studied in the case where the underlying source, not necessarily Gaussian, has finite differential entropy and the observation noise is Gaussian. The main result is a new lower bound for t ...

2019

GDSP: A graphical perspective on the distributed storage systems

Michael Christoph Gastpar, Saeid Sahraei

The classical distributed storage problem can be modeled by a k-uniform complete hyper-graph where vertices represent servers and hyper-edges represent users. Hence each hyper-edge should be able to recover the full file using only the memories of the vert ...

2017

Successive Refinement to Caching for Dynamic Requests

Michael Christoph Gastpar

In the celebrated coded caching problem studied by Maddah-Ali and Niesen, the peak-traffic network load is to be reduced by first caching some information about contents into individual memories of end users during the off-peak hours and then upon user req ...

IEEE2020

A Joint Typicality Approach to Compute–Forward

Michael Christoph Gastpar, Sung Hoon Lim, Adriano Pastore, Chen Feng

This paper presents a joint typicality framework for encoding and decoding nested linear codes in multi-user networks. This framework provides a new perspective on compute–forward within the context of discrete memoryless networks. In particular, it establ ...

2018

Computation in Multicast Networks: Function Alignment and Converse Theorems

Michael Christoph Gastpar, Naveen Goela

The classical problem in a network coding theory considers communication over multicast networks. Multiple transmitters send independent messages to multiple receivers that decode the same set of messages. In this paper, computation over multicast networks ...

Institute of Electrical and Electronics Engineers2016

Secure Transmission on the Two-Hop Relay Channel With Scaled Compute-and-Forward

Michael Christoph Gastpar, Jasper Goseling

In this paper, we consider communication on a two-hop channel in which a source wants to send information reliably and securely to the destination via a relay. We consider both the untrusted relay case and the external eavesdropper case. In the untrusted r ...

Institute of Electrical and Electronics Engineers2017

Random Access With Physical-Layer Network Coding

Michael Christoph Gastpar, Jasper Goseling

We consider a physical-layer network coding strategy for the random-access channel, based on compute-and-forward. When packets collide, it is possible to reliably recover a linear combination of the packets at the receiver. Over many rounds of transmission ...

Institute of Electrical and Electronics Engineers2015

Cooperative data exchange based on MDS codes

Michael Christoph Gastpar, Su Li

The coded cooperative data exchange problem is studied for the fully connected network. In this problem, each node initially only possesses a subset of the K packets making up the file. Nodes make broadcast transmissions that are received by all other node ...

2017

Private Retrieval, Computing, and Learning: Recent Progress and Future Challenges

Michael Christoph Gastpar

Most of our lives are conducted in the cyberspace. The human notion of privacy translates into a cyber notion of privacy on many functions that take place in the cyberspace. This article focuses on three such functions: how to privately retrieve informatio ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Differential Entropy of the Conditional Expectation under Gaussian Noise

Michael Christoph Gastpar, Alper Köse, Ahmet Arda Atalik

This paper considers an additive Gaussian noise channel with arbitrarily distributed finite variance input signals. It studies the differential entropy of the minimum mean-square error (MMSE) estimator and provides a new lower bound which connects the diff ...

IEEE2021

Robust Generalization via $\alpha$-Mutual Information

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

The aim of this work is to provide bounds connecting two probability measures of the same event using Rényi

\alpha

-Divergences and Sibson’s

\alpha

-Mutual Information, a generalization of respectively the Kullback-Leibler Divergence and Shannon’s Mutual ...

ETHZ2020

Robust Generalization via f−Mutual Information

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

Given two probability measures P and Q and an event E, we provide bounds on P(E) in terms of Q(E) and f-divergences. In particular, the bounds are instantiated when the measures considered are a joint distribution and the corresponding product of marginals ...

IEEE2020

The Distributed Karhunen-Loève Transform

Martin Vetterli, Michael Christoph Gastpar, Pier Luigi Dragotti

The Karhunen–Loève transform (KLT) is a key ele- ment of many signal processing and communication tasks. Many recent applications involve distributed signal processing, where it is not generally possible to apply the KLT to the entire signal; rather, the K ...

2006

Differential Entropy of the Conditional Expectation Under Additive Gaussian Noise

Michael Christoph Gastpar, Alper Köse, Ahmet Arda Atalik

The conditional mean is a fundamental and important quantity whose applications include the theories of estimation and rate-distortion. It is also notoriously difficult to work with. This paper establishes novel bounds on the differential entropy of the co ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Computation Over Gaussian Networks With Orthogonal Components

Michael Christoph Gastpar, Chien-Yi Wang

Function computation over Gaussian networks with orthogonal components is studied for arbitrarily correlated discrete memoryless sources. Two classes of functions are considered: 1) the arithmetic sum function and 2) the type function. The arithmetic sum f ...

Institute of Electrical and Electronics Engineers2014