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Michael Christoph Gastpar

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Publications associées (131)

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Capacity Scaling of Cognitive Networks: Beyond Interference-Limited Communication

Michael Christoph Gastpar

The capacity scaling laws of two overlaid networks are investigated, which are located in the same area sharing the same wireless resources with different priorities. The primary network can be regarded as an existing communication system operated in a lic ...

Institute of Electrical and Electronics Engineers2014

Lower bound on relaxed Wyner's Common Information

Michael Christoph Gastpar, Erixhen Sula

An important notion of common information between two random variables is due to Wyner. In this paper, we derive a lower bound on a relaxed variant of Wyner's common information for continuous random variables. The new bound reduces to the lower bound on W ...

IEEE2021

Strengthened Information-theoretic Bounds on the Generalization Error

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

The following problem is considered: given a joint distribution P XY and an event E, bound P XY (E) in terms of P X P Y (E) (where P X P Y is the product of the marginals of P XY ) and a measure of dependence of X and Y. Such bounds have direct application ...

IEEE2019

Increasing Availability in Distributed Storage Systems via Clustering

Michael Christoph Gastpar, Saeid Sahraei

We introduce the Fixed Cluster Repair System (FCRS) as a novel architecture for Distributed Storage Systems (DSS) that achieves a small repair bandwidth while guaranteeing a high availability. Specifically, we partition the set of servers in a DSS into s c ...

2018

Compute-and-forward for discrete memoryless networks

Michael Christoph Gastpar

Consider a receiver that observes multiple interfering codewords. The compute-and-forward technique makes it possible for the receiver to directly decode linear combinations of the codewords. Previous work has focused on compute-and-forward for linear Gaus ...

2014

On the Capacity of Large Gaussian Relay Networks

Martin Vetterli, Michael Christoph Gastpar

The capacity of a particular large Gaussian relay network is determined in the limit as the number of relays tends to infinity. Upper bounds are derived from cut-set arguments, and lower bounds follow from an argument involving uncoded transmission. It is ...

2005

K Users Caching Two Files: An Improved Achievable Rate

Michael Christoph Gastpar, Saeid Sahraei

Caching is an approach to smoothen the variability of traffic over time. Recently it has been proved that the local memories at the users can be exploited for reducing the peak traffic in a much more efficient way than previously believed. In this work we ...

Ieee2016

On Distributed Successive Refinement with Lossless Recovery

Michael Christoph Gastpar, Chien-Yi Wang

The problem of successive refinement in distributed source coding and in joint source-channel coding is considered. The emphasis is placed on the case where the sources have to be recovered losslessly in the second stage. In distributed source coding, it i ...

2014

Sum-Rate Capacity for Symmetric Gaussian Multiple Access Channels With Feedback

Michael Christoph Gastpar, Erixhen Sula

The feedback sum-rate capacity is established for the symmetric

J

-user Gaussian multiple-access channel (GMAC). The main contribution is a converse bound that combines the dependence-balance argument of Hekstra and Willems (1989) with a variant of the f ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Batch Universal Prediction

Michael Christoph Gastpar, Marco Bondaschi

Large language models (LLMs) have recently gained much popularity due to their surprising ability at generating human-like English sentences. LLMs are essentially predictors, estimating the probability of a sequence of words given the past. Therefore, it i ...

IEEE2024

Communication Versus Computation: Duality for Multiple-Access Channels and Source Coding

Michael Christoph Gastpar, Sung Hoon Lim, Jingge Zhu

Computation codes in network information theory are designed for scenarios where the decoder is not interested in recovering the information sources themselves, but only a function thereof. Körner and Marton showed for distributed source coding (DSC) that ...

2019

Information Theoretic Caching: The Multi-User Case

Michael Christoph Gastpar, Chien-Yi Wang, Sung Hoon Lim

In this paper, we present information theoretic inner and outer bounds on the fundamental tradeoff between cache memory size and update rate in a multi-user cache network. Each user is assumed to have individual caches, while upon users’ requests, an updat ...

Ieee2016

Privacy in Retrieval, Computing, and Learning

Michael Christoph Gastpar

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

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

Michael Christoph Gastpar, Martin Jaggi, Marco Bondaschi, Adway Girish, Ashok Vardhan Makkuva

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 ...

ICLR2025

Computation over Gaussian networks with orthogonal components

Michael Christoph Gastpar, Chien-Yi Wang

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 ...

2013

Functional Forwarding of Channel State Information

Michael Christoph Gastpar

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 ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Learning and Adaptive Data Analysis via Maximal Leakage

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

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 ...

IEEE2019

Approximate Sparsity Pattern Recovery: Information-Theoretic Lower Bounds

Michael Christoph Gastpar

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 ...

Institute of Electrical and Electronics Engineers2013

Common Information Components Analysis

Michael Christoph Gastpar, Erixhen Sula

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 ...

2021

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

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

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 ...

2023