Antoine Cornuéjols (AgroParisTech)

Antoine Cornuéjols

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

Research

Publications

Teaching (Mostly in French)

Research contracts

Responsibilities

PhD and Master Students

The EKINOCS Team

I am:

Full Professor in Computer Science at AgroParisTech,
head of the "Modélisation Mathématique, Informatique et Physique" (MMIP) department at AgroParisTech,
head of the LINK (Learning and INtegration of Knowledge) research team within the UMR-MIA 518 AgroParisTech-INRA research unit.

My fields of research lie in Machine Learning, data mining and, now somewhat remotedly, Cognitive Science. I am specially interested in the following topics :

Fundamentals of learning (more theoretically oriented)

	The geometry of learning
	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
	Transfer of information
	Analogical reasoning Which foundations for analogical reasoning ? Which links with inductive reasoning ? 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 ?
	Phase transition in inductive learning
	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 ?

Application oriented methodological developments

	Applications in data mining
	Scene analysis in changing environments Control of the memory in on-line learning Tracking Contour detection in the dynamical recognition of a moving kidney Boosting methods 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

Research

Publications

Teaching (Mostly in French)

Antoine Cornuéjols

The geometry of learning

Transfer of information

Phase transition in inductive learning

Applications in data mining