125 citations · 148 across the 19 of their papers we have counts for
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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…
stat.ML2025
On the Hardness of Reinforcement Learning with Transition Look-Ahead
Corentin Pla, Hugo Richard, Marc Abeille +2
We study reinforcement learning (RL) with transition look-ahead, where the agent may observe which states would be visited upon playing any sequence of actions before decidi…