Efficient learning of smooth probability functions from Bernoulli tests with guarantees.

We study the fundamental problem of learning an unknown, smooth probability function via pointwise Bernoulli tests. We provide a scalable algorithm for efficiently solving this problem with rigorous guarantees. In particular, we prove the convergence rate of our posterior update rule to the true probability function in L2-norm. Moreover, we allow the Bernoulli tests to depend on contextual features, and provide a modified inference engine with provable guarantees for this novel setting. Numerical results show that the empirical convergence rates match the theory, and illustrate the superiority of our approach in handling contextual features over the state-of-the-art.

Publié dans:
Proceedings of the International Conference on Machine Learning - ICML 2019
Présenté à:
36th International Conference on Machine Learning (ICML 2019), Long Beach, USA, June 9-15, 2019
Scheduled publication of Proceedings: Volume 97 is assigned to ICML 2019 (ISSN: 2640-3498)

 Notice créée le 2019-05-07, modifiée le 2019-08-28

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