Montag, 14. November 2022, 16:45 - 17:45 iCal

ISOR Colloquium

"MCMC-based Bayesian neural networks: Algorithmic and theoretical challenges"

Speaker: Mathias Trabs (Karlsruhe Institute of Technology, Germany)

HS 7 OMP1 (#1.303)
Oskar-Morgenstern-Platz 1, 1090 Wien

Vortrag

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Uncertainty estimation is a key issue when considering the application of deep neural network methods in science and engineering. In this talk, we introduce and benchmark a novel algorithm that quantifies epistemic uncertainty via Monte Carlo sampling from a tempered posterior distribution. It leverages a cyclic, adaptive temperature, as well as a stochastic Metropolis-Hastings step, to efficiently draw from the posterior. Empirically, we demonstrate successful uncertainty quantification and computational efficiency in a one-dimensional regression setting with additional focus on the detection of out-of distribution data. Based on PAC-Bayes theory, we analyse contraction rates and credibility of the posterior distribution.

The talk is based on joint works with Sebastian Bieringer, Gregor Kasieczka and Maximilian F. Steffen.

 

The talk also can be joined online via our ZOOM MEETING: univienna.zoom.us/j/63902528585

Meeting room opens at: November 14, 2022, 4.30 pm Vienna

Meeting ID: 639 0252 8585

Password: 506520

Zur Webseite der Veranstaltung

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Veranstalter

Institut für Statistik und Operations Research

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Kontakt

Sabine Sobotka-Tompits, BA
Fakultät für Wirtschaftswissenschaften
Institut für Statistik und Operations Research
+43 1 4277 38631
sabine.sobotka-tompits@univie.ac.at

Erstellt am Donnerstag, 27. Oktober 2022, 10:17
Letzte Änderung am Mittwoch, 09. November 2022, 10:29