Paper Detail
Ioannis Papageorgiou, Srinivas Nomula, Ayalvadi Ganesh, Sidharth Jaggi, Parimal Parag
We consider the problem of constructing a $K$-class classifier from the combination of $O(\log K)$ simple binary classifiers -- this is a natural paradigm to construct a sophisticated classifier in a distributed manner with each agent performing a relatively straightforward task. We study the fundamental performance limits of such a classifier when the corresponding binary classifiers are hyperplanes. For a stylized Gaussian setting where the $K$ class centers are independent Gaussian points in $\mathbb R^d$ and the observations are corrupted by Gaussian noise, we derive explicit performance bounds across several decoding and dimensional regimes. Extensive simulation experiments provide strong empirical validation of the presented theoretical results.
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@article{papageorgiou2026fundamental,
title = {Fundamental limits of distributed multiclass classification from simple binary decisions},
author = {Ioannis Papageorgiou and Srinivas Nomula and Ayalvadi Ganesh and Sidharth Jaggi and Parimal Parag},
year = {2026},
abstract = {We consider the problem of constructing a \$K\$-class classifier from the combination of \$O(\textbackslash{}log K)\$ simple binary classifiers -- this is a natural paradigm to construct a sophisticated classifier in a distributed manner with each agent performing a relatively straightforward task. We study the fundamental performance limits of such a classifier when the corresponding binary classifiers are hyperplanes. For a stylized Gaussian setting where the \$K\$ class centers are independent Gaussian points in },
url = {https://arxiv.org/abs/2607.19334},
keywords = {stat.ML, cs.IT, cs.LG, math.ST},
eprint = {2607.19334},
archiveprefix = {arXiv},
}
{}