6 papers
Non-Asymptotic Performance Guarantees for Neural Estimation of -Divergences
Sreejith Sreekumar, Zhengxin Zhang, Ziv Goldfeld
Statistical distances (SDs), which quantify the dissimilarity between probability distributions, are central to machine learning and statistics. A modern method for estimating such…
Communicate to Learn at the Edge
Deniz Gunduz, David Burth Kurka, Mikolaj Jankowski +3
Bringing the success of modern machine learning (ML) techniques to mobile devices can enable many new services and businesses, but also poses significant technical and research cha…
The Secrecy Capacity of Cost-Constrained Wiretap Channels
Sreejith Sreekumar, Alexander Bunin, Ziv Goldfeld +2
In many information-theoretic channel coding problems, adding an input cost constraint to the operational setup amounts to restricting the optimization domain in the capacity formu…
Strong Converse for Testing Against Independence over a Noisy channel
Sreejith Sreekumar, Deniz Gündüz
A distributed binary hypothesis testing (HT) problem over a noisy (discrete and memoryless) channel studied previously by the authors is investigated from the perspective of the st…
Privacy-aware Distributed Hypothesis Testing
Sreejith Sreekumar, Asaf Cohen, Deniz Gündüz
A distributed binary hypothesis testing (HT) problem involving two parties, a remote observer and a detector, is studied. The remote observer has access to a discrete memoryless so…
Distributed Hypothesis Testing Over Discrete Memoryless Channels
Sreejith Sreekumar, Deniz Gündüz
A distributed binary hypothesis testing (HT) problem involving two parties, one referred to as the observer and the other as the detector is studied. The observer observes a discre…