This paper investigates task-oriented communication for multi-device cooperative edge
inference, where a group of distributed low-end edge devices transmit the extracted features …

Given two random variables X and Y, an operational approach is undertaken to quantify the
“leakage” of information from X to Y. The resulting measure L (X→ Y) is called maximal …

Much of the field of Machine Learning exhibits a prominent set of failure modes, including
vulnerability to adversarial examples, poor out-of-distribution (OoD) detection …

The noisiness of a channel can be measured by comparing suitable functionals of the input
and output distributions. For instance, the worst case ratio of output relative entropy to input …

Given two discrete random variables X and Y, an operational approach is undertaken to
quantify the “leakage” of information from X to Y. The resulting measure ℒ (X→ Y) is called …

Common information is ubiquitous in information theory and related areas such as
theoretical computer science and discrete probability. However, because there are multiple …

We consider the problem of identifying universal low-dimensional features from high-
dimensional data for inference tasks in settings involving learning. For such problems, we …

With the unprecedented performance achieved by deep learning, it is commonly believed
that deep neural networks (DNNs) attempt to extract informative features for learning tasks …

We investigate the local differential privacy (LDP) guarantees of a randomized privacy
mechanism via its contraction properties. We first show that LDP constraints can be …

These inequalities can be tightened to produce" strong" data processing inequalities
(SDPIs), which are obtained by introducing appropriate channel-dependent or source …