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Efficient learning of smooth probability functions from Bernoulli tests with guarantees
Paul Rolland · Ali Kavis · Alexander Niklaus Immer · Adish Singla · Volkan Cevher

Wed Jun 12 04:30 PM -- 04:35 PM (PDT) @ Room 101

We study the fundamental problem of learning an unknown, smooth probability function via point-wise 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.

Author Information

Paul Rolland (Ecole Polytechnique Fédérale de Lausanne)
Ali Kavis (EPFL)
Alexander Niklaus Immer (EPFL, RIKEN)
Adish Singla (Max Planck Institute (MPI-SWS))
Adish Singla

Adish Singla is a faculty member at the Max Planck Institute for Software Systems (MPI-SWS), Germany, where he has been leading the Machine Teaching Group since 2017. He conducts research in the area of Machine Teaching, with a particular focus on open-ended learning and problem-solving domains. In recent years, his research has centered around developing AI-driven educational technology for introductory programming environments. He has received several awards for his research, including an AAAI Outstanding Paper Honorable Mention Award (2022) and an ERC Starting Grant (2021). He also has extensive experience working in the industry and is a recipient of several industry awards, including a research grant from Microsoft Research Ph.D. Scholarship Programme (2018), Facebook Graduate Fellowship (2015), Microsoft Tech Transfer Award (2011), and Microsoft Gold Star Award (2010).

Volkan Cevher (EPFL)

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