Michael Gastpar

Prof. Michael Gastpar

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Academic Career Length

18Years (2009-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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12 January 2022 in IEEE Journal on Selected Areas in Communications
Verified
Journal Article
Closed access
SUSennur Ulukus
Sennur Ulukus
SASalman Avestimehr
Salman Avestimehr
MGMichael Gastpar
Michael Gastpar
Syed JafarSyed Jafar
Syed Jafar
RTRavi Tandon
Ravi Tandon
CTChao Tian
Chao Tian
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 information from cyberspace (privacy in information retrieval), how to privately leverage large-scale distributed/parallel processing (privacy in distributed computing), and how to learn/train machine learning models from private data spread across multiple users (privacy in distributed (federated) learning). The article motivates each privacy setting, describes the problem formulation, summarizes breakthrough results in the history of each problem, and gives recent results and discusses some of the major ideas that emerged in each field. In addition, the cross-cutting techniques and interconnections between the three topics are discussed along with a set of open problems and challenges.
30 March 2017
Verified
Conference Paper
Closed access
CWChien-Yi Wang
Chien-Yi Wang
Sung Hoon LimSung Hoon Lim
Sung Hoon Lim
MGMichael Gastpar
Michael Gastpar
An information-theoretic lower bound is developed for the caching system studied by Maddah-Ali and Niesen. By comparing the proposed lower bound with the decentralized coded caching scheme of Maddah-Ali and Niesen, the optimal memory-rate tradeoff is characterized to within a multiplicative gap of 4.7 for the worst case, improving the previous analytical gap of 12. Furthermore, for the case when users' requests follow the uniform distribution, the multiplicative gap is tightened to 4.7, improving the previous analytical gap of 72. As an independent result of interest, for the single-user average case in which the user requests multiple files, it is proved that caching the most requested files is optimal.
31 May 2021 in IEEE Transactions on Information Theory
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Journal Article
Closed access
AEAmedeo Roberto Esposito
Amedeo Roberto Esposito
MGMichael Gastpar
Michael Gastpar
IIIbrahim Issa
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. These results find applications in adaptive data analysis, where multiple dependencies are introduced and in learning theory, where they can be employed to bound the generalization error of a learning algorithm. Bounds are given in terms of Sibson's Mutual Information, α-Divergences, Hellinger Divergences, and f-Divergences. A case of particular interest is the Maximal Leakage (or Sibson's Mutual Information of order infinity), since this measure is robust to post-processing and composes adaptively. The corresponding bound can be seen as a generalization of classical bounds, such as Hoeffding's and McDiarmid's inequalities, to the case of dependent random variables.
6 February 2019 in IEEE Transactions on Information Theory
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Journal Article
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KEKrishnan Eswaran
Krishnan Eswaran
MGMichael Gastpar
Michael Gastpar
28 September 2012 in Entropy
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Journal Article
Open access
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SJSang-Woon Jeon
Sang-Woon Jeon
MGMichael Gastpar
Michael Gastpar
In recent years, there has been rapid progress on understanding Gaussian networks with multiple unicast connections, and new coding techniques have emerged. The essence of multi-source networks is how to efficiently manage interference that arises from the transmission of other sessions. Classically, interference is removed by orthogonalization (in time or frequency). This means that the rate per session drops inversely proportional to the number of sessions, suggesting that interference is a strong limiting factor in such networks. However, recently discovered interference management techniques have led to a paradigm shift that interference might not be quite as detrimental after all. The aim of this paper is to provide a review of these new coding techniques as they apply to the case of time-varying Gaussian networks with multiple unicast connections. Specifically, we review interference alignment and ergodic interference alignment for multi-source single-hop networks and interference neutralization and ergodic interference neutralization for multi-source multi-hop networks. We mainly focus on the “degrees of freedom” perspective and also discuss an approximate capacity characterization.
15 November 2021 in IEEE Transactions on Information Theory
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Journal Article
Closed access
ESErixhen Sula
Erixhen Sula
MGMichael Gastpar
Michael Gastpar
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, our argument establishes the optimality of Gaussian codebooks and hence, a closed-form formula for the optimal rate region. The second problem is Wyner’s common information and a generalization thereof, where conditional independence is generalized to a limit on the conditional mutual information. We present full explicit solutions for the scalar as well as the vector case.