AAAI 2026technical0 citations
Online Linear Regression with Paid Stochastic Features
Nadav Merlis, Kyoungseok Jang, Nicolò Cesa-Bianchi
Abstract
We study an online linear regression setting in which the observed feature vectors are corrupted by noise and the learner can pay to reduce the noise level. In practice, this may happen for several reasons: for example, because features can be measured more accurately using more expensive equipment, or because data providers can be incentivized to release less private features. Assuming feature vectors are drawn i.i.d. from a fixed but unknown distribution, we measure the learner
BibTeX
@inproceedings{aaai2026_onlinelinearregr,
title = {Online Linear Regression with Paid Stochastic Features},
author = {Nadav Merlis and Kyoungseok Jang and Nicolò Cesa-Bianchi},
booktitle = {AAAI 2026},
year = {2026}
}