2 papers
cs.LG2025
Online Linear Regression with Paid Stochastic Features
Nadav Merlis, Kyoungseok Jang, Nicolò Cesa-Bianchi
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 h…
cs.LG2024
Sparsity-Agnostic Linear Bandits with Adaptive Adversaries
Tianyuan Jin, Kyoungseok Jang, Nicolò Cesa-Bianchi
We study stochastic linear bandits where, in each round, the learner receives a set of actions (i.e., feature vectors), from which it chooses an element and obtains a stochastic re…