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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5315 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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3355 Views

Lecture
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50:24

Multi-task feature learning

Andreas Argyriou

Feb 25, 2007

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7165 Views

Lecture
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19:38

Learning with structured inputs

Tong Zhang

Feb 25, 2007

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3378 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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4409 Views

Lecture
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36:15

Slow subspace learning from stationary processes

Andreas Maurer

Feb 25, 2007

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3125 Views

Lecture
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56:25

Multitask learning: the Bayesian way

Tom Heskes

Feb 25, 2007

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6012 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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8342 Views

Lecture
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01:20:01

Learning shared representations for object recognition

Antonio Torralba

Feb 25, 2007

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9657 Views

Lecture
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55:51

Output kernel tree

Florence d'Alche-Buc

Feb 25, 2007

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4555 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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4325 Views

Lecture
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20:40

XML structure mapping

Ludovic Denoyer

Feb 25, 2007

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5799 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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6913 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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3442 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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6518 Views

Lecture
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50:05

Learning Nonparametric Priors from Multiple Tasks

Shipeng Yu

Feb 25, 2007

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4386 Views

Lecture
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29:59

Targeted PDF Learning

John Shawe-Taylor

Feb 25, 2007

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4860 Views

Lecture