3 papers
cs.IT2026
One-Shot Information Theory via the Pairwise Error Probability: Lossy, Joint Source-Channel, Erasure, and Multiuser Coding
Nir Elkayam, Meir Feder
This paper extends a one-shot (finite-blocklength) information-theoretic framework built on a single primitive: the pairwise error probability (PEP) of a randomized, dither-broken…
cs.IT2026
A Pairwise-Error-Probability Framework for One-Shot Information Theory
Nir Elkayam, Meir Feder
We develop a one-shot (finite-blocklength) channel-coding framework based on the pairwise error probability (PEP) of a decoder with randomized tie-breaking. The tie-breaking rule y…
cs.LG2024
Batches Stabilize the Minimum Norm Risk in High Dimensional Overparameterized Linear Regression
Shahar Stein Ioushua, Inbar Hasidim, Ofer Shayevitz +1
Learning algorithms that divide the data into batches are prevalent in many machine-learning applications, typically offering useful trade-offs between computational efficiency and…