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NTUA STATISTICS SEMINAR:  Markov chain Monte Carlo sampling for machine learning and inverse problems by Omiros Papaspiliopoulos  Forumgrstats

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Λέσχη Φίλων Στατιστικής - GrStats forum
NTUA STATISTICS SEMINAR:  Markov chain Monte Carlo sampling for machine learning and inverse problems by Omiros Papaspiliopoulos  Forumgrstats
Λέσχη Φίλων Στατιστικής - GrStats forum
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NTUA STATISTICS SEMINAR:  Markov chain Monte Carlo sampling for machine learning and inverse problems by Omiros Papaspiliopoulos  Empty Αναβολή Ομιλίας

Wed 17 May 2017 - 0:20
Λόγω της αυριανής απεργίας η ομιλία του Όμηρου Παπασπηλιόπουλου αναβάλλεται.
Θα βγει ανακοίνωση για τη νέα ημερομηνία και ώρα.
grstats
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Posts : 957
Join date : 2009-10-21
http://stat-athens.aueb.gr/~grstats/

NTUA STATISTICS SEMINAR:  Markov chain Monte Carlo sampling for machine learning and inverse problems by Omiros Papaspiliopoulos  Empty NTUA STATISTICS SEMINAR: Markov chain Monte Carlo sampling for machine learning and inverse problems by Omiros Papaspiliopoulos

Thu 11 May 2017 - 0:58
Omiros Papaspiliopoulos (ICREA research professor, based at UPF)
Ημερομηνία: Τετάρτη 17/5
Ώρα: 12:30μμ
Αίθουσα: Σεμιναρίων του Τομέα Μαθηματικών ΣΕΜΦΕ

Title: Markov chain Monte Carlo sampling for machine learning and inverse problems

Abstract: I will give a synthetic overview of the challenges, objectives and the state-of-the-art for prediction and uncertainty quantification using Markov chain Monte Carlo in Bayesian inverse problems and in machine learning. I will first show how some standard problems in inverse problems and machine learning can be formulated as problems of simulating from high (or even infinite) dimensional change of Gaussian measure. I will then show how Monte Carlo simulation algorithms can be constructed by discretising the Langevin stochastic differential equation and highlight the two most popular algorithms, the so-called preconditioned Metropolis-adjsuted Langevin algorithm (pMALA) and the preconditioned Crank-Nicolson Langevin (pcNL) algorithm. I will then refer to some recent work jointly with Michalis Titsias (Computer Science, AUEB) that has produced algorithms that achieve enormous efficiency gains relative to the state-of-the-art and demonstrate their success in high-dimensional regression and classification problems
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