Open House on Multi-Task and Complex Outputs Learning, London 2006

Open House on Multi-Task and Complex Outputs Learning, London 2006

18 Lectures · Jul 9, 2006

About

The Open House is part of the PASCAL's fifth thematic programme. This scientific programme brings together researchers around two themes:

  • learning tasks where the target is complex (i.e. large number of different predictions) but has structure (sequence, tree, graph) that can be utilized for learning effectively;
  • multi-task learning, where several dependent learning tasks are to be tackled at once.

In particular, we shall focus on the following research topics/issues.

Related categories

Uploaded videos:

Introductions

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07:54

Introduction

Massimiliano Pontil

Feb 25, 2007

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5316 Views

Introduction

Lectures

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01:04:00

Inductive transfer via embeddings into a common feature space

Shai Ben-David

Feb 25, 2007

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3356 Views

Lecture
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50:24

Multi-task feature learning

Andreas Argyriou

Feb 25, 2007

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7167 Views

Lecture
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19:38

Learning with structured inputs

Tong Zhang

Feb 25, 2007

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3380 Views

Lecture
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48:05

Estimation of gradients and coordinate covariation in classification

Sayan Mukherjee

Feb 25, 2007

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4411 Views

Lecture
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36:15

Slow subspace learning from stationary processes

Andreas Maurer

Feb 25, 2007

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3126 Views

Lecture
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56:25

Multitask learning: the Bayesian way

Tom Heskes

Feb 25, 2007

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6013 Views

Lecture
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01:08:00

Top-down vs. bottom-up methods for hierarchical classification

Claudio Gentile

Feb 25, 2007

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8343 Views

Lecture
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01:20:01

Learning shared representations for object recognition

Antonio Torralba

Feb 25, 2007

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9660 Views

Lecture
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55:51

Output kernel tree

Florence d'Alche-Buc

Feb 25, 2007

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4556 Views

Lecture
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47:57

Efficient max-margin Markov learning via conditional gradient and probabilistic ...

Juho Rousu

Feb 25, 2007

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4327 Views

Lecture
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20:40

XML structure mapping

Ludovic Denoyer

Feb 25, 2007

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5800 Views

Lecture
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01:03:55

Going beyond bag-of-words: dealing with a text as a graph of triples

Marko Grobelnik

Feb 25, 2007

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8723 Views

Lecture
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31:47

Machine Learning for Sequential Data: A Comparative Study with Applications to N...

Sandor Canisius

Feb 25, 2007

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6914 Views

Lecture
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13:48

Learning Structured Outputs via Kernel Dependency Estimation and Stochastic Gram...

Andrea Passerini

Feb 25, 2007

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3443 Views

Lecture
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24:56

How to Teach Support Vector Machine to Learn Vector Outputs

Sandor Szedmak

Feb 25, 2007

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6520 Views

Lecture
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50:05

Learning Nonparametric Priors from Multiple Tasks

Shipeng Yu

Feb 25, 2007

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4388 Views

Lecture
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29:59

Targeted PDF Learning

John Shawe-Taylor

Feb 25, 2007

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4861 Views

Lecture