André Elisseeff

Publication Activity (10 Years)

Years Active: 1996-2012
Publications (10 Years): 0

Top Topics

Observational Data

Causal Bayesian Networks

Graphical Models

Top Venues

Publications

Ulf H. Nielsen, Jean-Philippe Pellet, André Elisseeff
Explanation Trees for Causal Bayesian Networks CoRR (2012)
Giuseppe Paleologo, André Elisseeff, Gianluca Antonini
Subagging for credit scoring models. Eur. J. Oper. Res. 201 (2) (2010)
André Elisseeff, Jean-Philippe Pellet, Eleni Pratsini
Causal networks for risk and compliance: Methodology and application. IBM J. Res. Dev. 54 (3) (2010)
Ulf H. Nielsen, Jean-Philippe Pellet, André Elisseeff
Explanation Trees for Causal Bayesian Networks. UAI (2008)
Isabelle Guyon, Constantin F. Aliferis, Gregory F. Cooper, André Elisseeff, Jean-Philippe Pellet, Peter Spirtes, Alexander R. Statnikov
Design and Analysis of the Causation and Prediction Challenge. WCCI Causation and Prediction Challenge (2008)
Jean-Philippe Pellet, André Elisseeff
Finding Latent Causes in Causal Networks: an Efficient Approach Based on Markov Blankets. NIPS (2008)
Jean-Philippe Pellet, André Elisseeff
Using Markov Blankets for Causal Structure Learning. J. Mach. Learn. Res. 9 (2008)
Jean-Philippe Pellet, André Elisseeff
A Partial Correlation-Based Algorithm for Causal Structure Discovery with Continuous Variables. IDA (2007)
Olivier Bousquet, André Elisseeff
Guest editorial: Learning theory. Mach. Learn. 66 (2-3) (2007)
Giuliano Tirenni, Abderrahim Labbi, André Elisseeff, Cesar Berrospi
Efficient Allocation of Marketing Resources using Dynamic Programming. SDM (2005)
André Elisseeff, Theodoros Evgeniou, Massimiliano Pontil
Stability of Randomized Learning Algorithms. J. Mach. Learn. Res. 6 (2005)
Jason Weston, Christina S. Leslie, Eugene Ie, Dengyong Zhou, André Elisseeff, William Stafford Noble
Semi-supervised protein classification using cluster kernels. Bioinform. 21 (15) (2005)
Yann Guermeur, Gianluca Pollastri, André Elisseeff, Dominique Zelus, Hélène Paugam-Moisy, Pierre Baldi
Combining protein secondary structure prediction models with ensemble methods of optimal complexity. Neurocomputing 56 (2004)
Theodoros Evgeniou, Massimiliano Pontil, André Elisseeff
Leave One Out Error, Stability, and Generalization of Voting Combinations of Classifiers. Mach. Learn. 55 (1) (2004)
Jason Weston, Fernando Pérez-Cruz, Olivier Bousquet, Olivier Chapelle, André Elisseeff, Bernhard Schölkopf
Feature selection and transduction for prediction of molecular bioactivity for drug design. Bioinform. 19 (6) (2003)
Jason Weston, Christina S. Leslie, Dengyong Zhou, André Elisseeff, William Stafford Noble
Semi-supervised Protein Classification Using Cluster Kernels. NIPS (2003)
Jason Weston, André Elisseeff, Bernhard Schölkopf, Michael E. Tipping
Use of the Zero-Norm with Linear Models and Kernel Methods. J. Mach. Learn. Res. 3 (2003)
Isabelle Guyon, André Elisseeff
An Introduction to Variable and Feature Selection. J. Mach. Learn. Res. 3 (2003)
Olivier Bousquet, André Elisseeff
Stability and Generalization. J. Mach. Learn. Res. 2 (2002)
Savina Andonova, André Elisseeff, Theodoros Evgeniou, Massimiliano Pontil
A Simple Algorithm for Learning Stable Machines. ECAI (2002)
Asa Ben-Hur, André Elisseeff, Isabelle Guyon
A Stability Based Method for Discovering Structure in Clustered Data. Pacific Symposium on Biocomputing (2002)
Jason Weston, Olivier Chapelle, André Elisseeff, Bernhard Schölkopf, Vladimir Vapnik
Kernel Dependency Estimation. NIPS (2002)
Nello Cristianini, John Shawe-Taylor, André Elisseeff, Jaz S. Kandola
On Kernel-Target Alignment. NIPS (2001)
André Elisseeff, Jason Weston
A kernel method for multi-labelled classification. NIPS (2001)
Hélène Paugam-Moisy, André Elisseeff, Yann Guermeur
Generalization Performance of Multiclass Discriminant Models. IJCNN (4) (2000)
Yann Guermeur, André Elisseeff, Hélène Paugam-Moisy
A New Multi-Class SVM Based on a Uniform Convergence Result. IJCNN (4) (2000)
Olivier Bousquet, André Elisseeff
Algorithmic Stability and Generalization Performance. NIPS (2000)
André Elisseeff, Hélène Paugam-Moisy
JNN, a randomized algorithm for training multilayer networks in polynomial time. Neurocomputing 29 (1-3) (1999)
André Elisseeff, Hélène Paugam-Moisy
Size of Multilayer Networks for Exact Learning: Analytic Approach. NIPS (1996)