Antoine Cornuéjols
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Professeur / Full Professor AgroParisTech
Dept. MMIP (Modélisation Mathématique, Informatique et Physique)
16, rue Claude Bernard
75005 Paris Cedex (France)
Tel : +33 (0)1-44-08-72-29
email : antoine.cornuejols [at] agroparistech.fr
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I am Full Professor in Computer Science at AgroParisTech
(INA-PG), associated with the "Modélisation Mathématique, Informatique
et Physique" (MMIP) research department, and a corresponding member of
the TAO research team at LRI (Laboratoire
de Recherche en Informatique) at the Université de Paris-Sud, Orsay
(France). My fields of research lie in Machine
Learning, data
mining
and Cognitive Science. I am specially
interested in the following topics :
Fundamentals of learning (more
theoretically oriented)
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The geometry of learning
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- What is the space of programs when the change
operator is learning from examples ?
- What
kind of dot products can yield negative values (as opposed to the
mutual information which is not fit for learning) ?
- What
is a proper metrics ?
- How
does order sensitivity in learning relate to curved space ?
- Teachability
- How to best order a data sequence
for a specific goal ?
- Which data should be presented
?
- In which order ?
- At which speed ?
- On-line
learning
- Incremental
learning (in a stationary environment)
- With covariate shift
- Tracking
- Concept-drift
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Transfer of information
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- Analogical reasoning
- Which foundations for analogical
reasoning ?
- Which links with inductive reasoning
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- Learning of new conceptual domains
- Which
articulations with known domains allow to learn a new conceptual
domain ?
- How a conceptual domain in the
learning come to get integrated with other conceptual domains ?
- What are the effects of limited transmission
bandwith in a collection of learning agents ?
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Phase transition in inductive learning
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- What is the influence of language on learning
new knowledge ?
- How is inductive supervised learning affected
by the description language ?
- What explanation for phase transitions
in supervised learning ?
- How to alleviate these with changes of
representation language ?
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Application oriented methodological developments
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Applications in data mining
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- Scene
analysis in changing environments
- Control
of the memory in on-line learning
- Tracking
- Contour
detection in the dynamical recognition of a moving kidney
- Text
analysisin a risk assessment task
- Automatic
detection
of parts of texts related to a risk assessement qualification
- Automatic
assessement of uncertainty in parts of text
- Protein
docking
- Learning
from positive instances only
- Reduction
of the false positive rate
- Use
of abstraction and change of granularity in data mining methods
- Study
of genomics and microarray data
- Combination of
weak selection methods
- Active methods
for feature selection
- Study
of cardiovascular accidents (prediction of)
- Methods
for learning in the presence of imbalanced data sets and asymetrical
labelling noise
- Asymetrical
semi-supervised learning
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