activity
20162025
most citedBeyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications

26 citations · 45 across the 7 of their papers we have counts for

collaborators

12 papers

cs.LG2025

Hierarchical Forecast Reconciliation on Networks: A Network Flow Optimization Formulation

Charupriya Sharma, Iñaki Estella Aguerri, Daniel Guimarans

Hierarchical forecasting with reconciliation requires forecasting values of a hierarchy (e.g.~customer demand in a state and district), such that forecast values are linked (e.g.~…

cs.IT2022★ 26 cited

Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications

Deniz Gunduz, Zhijin Qin, Inaki Estella Aguerri +5

Communication systems to date primarily aim at reliably communicating bit sequences. Such an approach provides efficient engineering designs that are agnostic to the meanings of th…

cs.IT2019

Optimal Rate-Exponent Region for a Class of Hypothesis Testing Against Conditional Independence Problems

Abdellatif Zaidi, Inaki Estella Aguerri

We study a class of distributed hypothesis testing against conditional independence problems. Under the criterion that stipulates minimization of the Type II error rate subject to…

cs.IT2019

Vector Gaussian CEO Problem Under Logarithmic Loss

Yigit Ugur, Inaki Estella Aguerri, Abdellatif Zaidi

In this paper, we study the vector Gaussian Chief Executive Officer (CEO) problem under logarithmic loss distortion measure. Specifically, agents observe independently c…

cs.IT2018

Vector Gaussian CEO Problem Under Logarithmic Loss and Applications

Yigit Ugur, Inaki Estella Aguerri, Abdellatif Zaidi

We study the vector Gaussian Chief Executive Officer (CEO) problem under logarithmic loss distortion measure. Specifically, agents observe independently corrupted Gaussi…

stat.ML2018

Distributed Variational Representation Learning

Inaki Estella Aguerri, Abdellatif Zaidi

The problem of distributed representation learning is one in which multiple sources of information are processed separately so as to learn as much information as p…