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Active Learning Challenge . Isabelle Guyon (Clopinet, California)

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active learning challenge

Active Learning Challenge

Isabelle Guyon (Clopinet, California)

Gavin Cawley (University of East Anglia, UK) Olivier Chapelle (Yahhoo!, California) Gideon Dror (Academic College of Tel-Aviv-Yaffo, Israel) Vincent Lemaire (Orange, France) Amir Reza Saffari Azar (Graz University of Technology) Alexander Statnikov (New York University, USA)

what is the problem

What is the problem?

labeling data is expensive
Labeling data is expensive





Labeling data

examples of domains
Examples of domains
  • Chemo-informatics
  • Handwriting and speech recognition
  • Image processing
  • Text processing
  • Marketing
  • Ecology
  • Embryology

what is active learning
What is active learning?

what is out there

What is out there?


Burr Settles. Active Learning Literature Survey.

CDTR 1648, Univ. Wisconsin–Madison. 2009.

de novo queries
“De novo” queries

De novo queries implicitly assume interventions on the system under study: not for this challenge

focus on pool based al
Focus on “pool-based” AL
  • Simplest scenario for a challenge.

Training data: labels can be queried

Test data: unknown labels

  • Methods developed for pool-based AL should also be useful for stream-based AL.




  • Toy 2-class problem, 400 instances Gaussian distributed.
  • Linear logistic regression model trained w. 30 random instances.
  • (c) Linear logistic regression model trained w. 30 actively queried
  • instances using “uncertainty sampling”.

Burr Settles, 2009

learning curve
Learning curve

Burr Settles, 2009

other methods
Other methods
  • Expected model change (greatest gradient if sample were used for training)
  • Query by committee (query the sample subject to largest disagreement)
  • Bayesian active learning (maximize change in revised posterior distribution)
  • Expected error reduction (maximize generalization performance improvement)
  • Information density (ask for examples both informative and representative)

Burr Settles, 2009



data donors
Data donors

This project would not have been possible without generous donations of data:

  • Chemoinformatics -- Charles Bergeron, Kristin Bennett and Curt Breneman (Rensselaer Polytechnic Institute, New York) contributed a dataset, which will be used for final testing.
  • Embryology --Emmanuel Faure, Thierry Savy, Louise Duloquin, Miguel Luengo Oroz, Benoit Lombardot, Camilo Melani, Paul Bourgine, and Nadine Peyriéras (Institut des systèmes complexes, France) contributed the ZEBRA dataset.
  • Handwriting recognition -- Reza Farrahi Moghaddam, Mathias Adankon, Kostyantyn Filonenko, Robert Wisnovsky, and Mohamed Chériet (Ecole de technologie supérieure de Montréal, Quebec) contributed the IBN_SINA dataset.
  • Marketing --Vincent Lemaire, Marc Boullé, Fabrice Clérot, Raphael Féraud, Aurélie Le Cam, and Pascal Gouzien (Orange, France) contributed the ORANGE dataset, previously used in the KDD cup 2009.

We also reused data made publicly available on the Internet:

  • Chemoinformatics --The National Cancer Institute (USA) for the HIVA dataset.
  • Ecology -- Jock A. Blackard, Denis J. Dean, and Charles W. Anderson (US Forest Service, USA) for the SYLVA dataset (Forest cover type).
  • Text processing -- Tom Mitchell (USA) and Ron Bekkerman (Israel) for the NOVA datset (derived from the Twenty Newsgroups).

development datasets
Development datasets

  • Spase data
  • Missing values
  • Unbalanced classes
  • Categorical variables
  • Noisy data
  • Large datasets

final test datasets
Final test datasets
  • Will serve to do the final ranking
  • Will be from the same domains
  • May have different data representations and distributions
  • No feed-back: the results will not be revealed until the end of the challenge



virtual lab
Virtual Lab

Virtual cash

  • Joint work with:
  • Constantin Aliferis, New York University
  • Gregory F. Cooper, Pittsburg University
  • André Elisseeff, Nhumi, Zürich
  • Jean-Philippe Pellet, IBM Zürich
  • Alexander Statnikov, New York University
  • Peter Spirtes, Carnegie Mellon

step by step instructions
Step by step instructions

Download the data. You get 1 labeled example.

  • Predict
  • Sample
  • Submit a query
  • Retrieve the labels

two phases
Two phases
  • Development phase:
    • 6 datasets available
    • Can try as many times as you want
    • Matlab users can run queries on their computers
    • Others can use the labels (provided)
  • Final test phase:
    • 6 new datasets available
    • A single try
    • No feed-back



auc score
AUC score

For each set of samples queried, we assess the predictions of the learning machine with the Area under the ROC curve.

area under the learning curve alc
Area under the Learning Curve(ALC)

Linear interpolation. Horizontal extrapolation.

One query

Five queries

Thirteen queries

Lazy: ask for all labels at once


If you win on…

  • 1 dataset: $100
  • 2 datasets: $200
  • 3 datasets: $400
  • 4 datasets: $800
  • 5 datasets: $1600
  • 6 datasets: $3200!
  • Plus travel awards for top ranking students.



Try our new challenge, learn, and win!!!!

  • Workshops:
    • AISTATS 2010, Sardinia, May, 2010
    • WCCI 2010 Workshop, Barcelona, July, 2010
    • Travel awards for top ranking students.
  • Proceedings published by JMLR & IEEE.
  • Prizes: P(i)=$100 * 2(n-1)
  • Your problem solved by dozens of research groups:
    • Help us organize the next challenge!