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What and Who
Title:Principled Transfer Learning
Speaker:Christoph Lampert
coming from:IST Austria
Speakers Bio:
Event Type:Talk
Visibility:D1, D2, D3, D4, D5, RG1, SWS, MMCI
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Level:Public Audience
Date, Time and Location
Date:Wednesday, 13 June 2018
Duration:60 Minutes
Building:E1 5
For most currently successful machine learning techniques performance guarantees are only available when assuming an "i.i.d." scenario: one is given a training set consisting of independent and identically distributed samples, and one aims at building a model that will work well on future samples from the same distribution. Unfortunately, in many real-world situations the i.i.d. property does not hold, causing existing techniques to become unreliable or outright fail.

In my talk, I will discuss recent results from our group for situations in which the "future" data distribution differs from the distribution of the available training data. In particular, I will highlight new theoretical guarantees we were able to obtain for the situation of multi-task learning, and I will show how such theoretical results can give rise to new and principled machine learning algorithms.

Name(s):Connie Balzert
Video Broadcast
Video Broadcast:NoTo Location:
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Connie Balzert/MPI-INF, 06/08/2018 12:36 PM
Last modified:
Uwe Brahm/MPII/DE, 06/13/2018 07:01 AM
  • Connie Balzert, 06/08/2018 12:36 PM -- Created document.