3 papers
cs.LG2026
Heavy-Tailed and Long-Range Dependent Noise in Stochastic Approximation: A Finite-Time Analysis
Siddharth Chandak, Anuj Yadav, Ayfer Ozgur +1
Stochastic approximation (SA) is a fundamental iterative framework with broad applications in reinforcement learning and optimization. Classical analyses typically rely on martinga…
cs.IT2026
Log-Likelihood Loss for Semantic Compression
Anuj Kumar Yadav, Dan Song, Yanina Shkel +1
We study lossy source coding under a distortion measure defined by the negative log-likelihood induced by a prescribed conditional distribution . This \emph{log-likelihood…
eess.SP2025
Majority Vote Compressed Sensing
Henrik Hellström, Jiwon Jeong, Ayfer Ãzgür +2
We consider the problem of non-coherent over-the-air computation (AirComp), where devices carry high-dimensional data vectors of sparsity $\lVert\…