Machine Learning Challenges



Machine Learning is the science of building hardware or software that can achieve tasks by learning from examples. The examples often come as {input, output} pairs. Given new inputs a trained machine can make predictions of the unknown output.

Examples of machine learning tasks include:


We organize challenges to stimulate research in this field. The web sites of past challenges remain open for post-challenge submission as even-going benchmarks.


   Feature selection (NIPS 2003):

Seventy five participants competed on five classification problems to make best predictions and select the smallest possible subset of relevant input variables (features). The tasks include: cancer diagnosis from mass-spectrometry data, handwritten digit recognition, text classification, and drug discovery.

Challenge web site

Workshop page

Result page

Book edited (CD with data and code)

Matlab software and course material


   Performance prediction (WCCI 2006):

One hundred and forty-five five participants competed on five classification problems to make best predictions and predict their generalization performance on new unseen data. The tasks include: marketing, drug discovery, text classification, handwritten digit recognition, and ecology.

Challenge web site

Workshop page


Matlab software

Call for papers


   Agnostic learning vs. prior knowledge (NIPS 2006 and IJCNN 2007):

This challenge has two tracks: the agnostic learning track and the prior knowledge track, corresponding to two versions of five datasets. The “agnostic track” version of the data is ready-to-use data preprocessed in a feature-based representation suitable for off-the-shelf machine learning packages. The identity of the features is not revealed. The “prior knowledge track” version of the data is just raw data, not always in a feature representation, coming with information about the nature and source of the data. Can you do better with the raw data and prior knowledge about the task? How far can you get with pure “black box learning”?

Challenge web site

NIPS 2006 workshop page

IJCNN 2007 workshop page


Book in preparation

   Learning causal dependencies (WCCI 2008 and NIPS 2008):

What affects your health? What affects the economy? What affects climate changes? and… which actions will have beneficial effects? This series of competitions challenge the participants to discover the causes of given effects, based on observational data. The datasets include re-simulation data from models closely resembling real systems and real data for which the causal dependencies are known from experimental evidence.

Challenge web site

WCCI 2008 workshop page

NIPS2008 workshop page

JMLR W&CP proceedings vol 3

JMLR W&CP proceedings vol 6

    Fast scoring in a large database  (KDD cup 2009):

Customer Relationship Management (CRM) is a key element of modern marketing strategies. The KDD Cup 2009 offered the opportunity to work on large marketing databases from the French Telecom company Orange to predict the propensity of customers to switch provider (churn), buy new products or services (appetency), or buy upgrades or add-ons proposed to them to make the sale more profitable (up-selling).

  Challenge web site

  KDD cup 2009 workshop page
JMLR W&CP proceedings vol 7
     Active Learning Challenge  (AISTATS 2010 and WCCI 2010):

Labeling data is expensive, but large amounts of unlabeled data are available at low cost. Such problems might be tackled from different angles: learning from unlabeled data or active learning. In the former case, the algorithms must satisfy themselves with the limited amount of labeled data and capitalize on the unlabeled data with semi-supervised learning methods. In the latter case, the algorithms may place a limited number of queries to get labels. The goal in that case is to optimize the queries to label data and the problem is referred to as active learning.

Challenge website
AISTATS 2010 workshop
WCCI 2010 workshop

We are very grateful to our sponsors:


  Pascal network Unipen        ETH MisterP   Microsoft KXEN   HDC  U southampton    Predicant  GoogleOrange NSF  

This project is supported by the National Science Foundation under Grants N0. ECCS-0424142, ECCS-0736687 and ECCS-0725746. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.