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We investigate online classification with paid stochastic experts. Here,before making their prediction, each expert must be paid. The amount that wepay each expert directly influences the accuracy of their prediction throughsome unknown Lipschitz "productivity" function. In each round, the learner mustdecide how much to pay each expert and then make a prediction. They incur acost equal to a weighted sum of the prediction error and upfront payments forall experts. We introduce an online learning algorithm whose total cost after$T$ rounds exceeds that of a predictor which knows the productivity of allexperts in advance by at most $\mathcal{O}(K^2(\log T)\sqrt{T})$ where $K$ isthe number of experts. In order to achieve this result, we combine Lipschitzbandits and online classification with surrogate losses. These tools allow usto improve upon the bound of order $T^{2/3}$ one would obtain in the standardLipschitz bandit setting. Our algorithm is empirically evaluated on syntheticdata

Trading-Off Payments and Accuracy in Online Classification with Paid Stochastic Experts