From the 1 of 4 linked papers with an AI index.
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On The Most Discriminative Boolean Functions for Correlated Sources
Jun Chen, Shun Watanabe, Lei Yu
The paper investigates which pairs of Boolean functions best separate two correlated binary sources by maximizing Kullback-Leibler divergence (and Fisher information), proving that…
Rate-Distortion-Perception Theory for the Quadratic Wasserstein Space
Xiqiang Qu, Jun Chen, Lei Yu +1
We establish a single-letter characterization of the fundamental distortion-rate-perception tradeoff with limited common randomness under the squared error distortion measure and t…
On the Fundamental Limits of Integrated Sensing and Communications Under Logarithmic Loss
Jun Chen, Lei Yu, Yonglong Li +3
We study a unified information-theoretic framework for integrated sensing and communications (ISAC), applicable to both monostatic and bistatic sensing scenarios. Special attention…
Channel-Aware Optimal Transport: A Theoretical Framework for Generative Communication
Xiqiang Qu, Ruibin Li, Jun Chen +2
Optimal transport has numerous applications, particularly in machine learning tasks involving generative models. In practice, the transportation process often encounters an informa…
Gaussian Rate-Distortion-Perception Coding and Entropy-Constrained Scalar Quantization
Li Xie, Liangyan Li, Jun Chen +2
This paper investigates the best known bounds on the quadratic Gaussian distortion-rate-perception function with limited common randomness for the Kullback-Leibler divergence-based…