Machine Learning Coffee seminar "Differentially Private Bayesian Learning"

Tapahtuman tyyppi: 
HIIT seminaari
06.03.2017 - 09:15 - 10:00
Antti Honkela
Konemiehentie 2, seminar room T6

This week's speaker at the Machine Learning Coffee seminar is Antti Honkela, Assistant Professor, University of Helsinki.

Differentially Private Bayesian Learning

Abstract: Many applications of machine learning for example in health care would benefit from methods that can guarantee data subject privacy. Differential privacy has recently emerged as a leading framework for private data analysis. Differential privacy guarantees privacy by requiring that the results of an algorithm should not change much even if one data point is changed, thus providing plausible deniability for the data subjects.

In this talk I will present methods for efficient differentially private Bayesian learning. In addition to asymptotic efficiency, we will focus on how to make the methods efficient for moderately-sized data sets. The methods are based on perturbation of sufficient statistics for exponential family models and perturbation of gradients for variational inference. Unlike previous state-of-the-art, our methods can predict drug sensitivity of cancer cell lines using differentially private linear regression with better accuracy than using a very small non-private data set.

Machine Learning Coffee seminars are weekly seminars held jointly by the Aalto University and the University of Helsinki. The seminars aim to gather people from different fields of science with interest in machine learning. Seminars will be held on Mondays at 9 am at Aalto University and the University of Helsinki every other week. At Aalto University, talks will be held in Konemiehentie 2, seminar room T5 and at the University of Helsinki in Kumpula, seminar room D123, unless otherwise noted. Talks will begin at 9:15 am and porridge and coffee will be served from 9:00 am.

Next talks:

  • March 13, Kumpula, Tapio Pahikkala: Small data AUC estimation of machine learning methods: Pitfalls and remedies
  • March 20, Otaniemi, Harri Valpola: Semi-supervised Deep Learning
  • March 27, Kumpula, Hiroshi Mamitsuka: Learning to Rank: applications to bioinformatics
  • April 4, Otaniemi: Mikko Kivelä: Multilayer networks


04.03.2017 - 11:52 Teemu Roos
04.03.2017 - 11:47 Teemu Roos