André Elisseeff

Ulf H. Nielsen, Jean-Philippe Pellet, André Elisseeff: Explanation Trees for Causal Bayesian Networks. CoRR abs/1206.3276 (2012)

Giuseppe Paleologo, André Elisseeff, Gianluca Antonini: Subagging for credit scoring models. European Journal of Operational Research 201(2): 490-499 (2010)

André Elisseeff, Jean-Philippe Pellet, Eleni Pratsini: Causal networks for risk and compliance: Methodology and application. IBM Journal of Research and Development 54(3): 6 (2010)

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. Journal of Machine Learning Research - Proceedings Track 3: 1-33 (2008)

Jean-Philippe Pellet, André Elisseeff: Using Markov Blankets for Causal Structure Learning. Journal of Machine Learning Research 9: 1295-1342 (2008)

Jean-Philippe Pellet, André Elisseeff: Finding Latent Causes in Causal Networks: an Efficient Approach Based on Markov Blankets. NIPS 2008: 1249-1256

Ulf H. Nielsen, Jean-Philippe Pellet, André Elisseeff: Explanation Trees for Causal Bayesian Networks. UAI 2008: 427-434

Olivier Bousquet, André Elisseeff: Guest editorial: Learning theory. Machine Learning 66(2-3): 115-118 (2007)

Jean-Philippe Pellet, André Elisseeff: A Partial Correlation-Based Algorithm for Causal Structure Discovery with Continuous Variables. IDA 2007: 229-239

Jason Weston, Christina S. Leslie, Eugene Ie, Dengyong Zhou, André Elisseeff, William Stafford Noble: Semi-supervised protein classification using cluster kernels. Bioinformatics 21(15): 3241-3247 (2005)

André Elisseeff, Theodoros Evgeniou, Massimiliano Pontil: Stability of Randomized Learning Algorithms. Journal of Machine Learning Research 6: 55-79 (2005)

Giuliano Tirenni, Abderrahim Labbi, André Elisseeff, Cesar Berrospi: Efficient Allocation of Marketing Resources using Dynamic Programming. SDM 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: 305-327 (2004)

Theodoros Evgeniou, Massimiliano Pontil, André Elisseeff: Leave One Out Error, Stability, and Generalization of Voting Combinations of Classifiers. Machine Learning 55(1): 71-97 (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. Bioinformatics 19(6): 764-771 (2003)

Isabelle Guyon, André Elisseeff: An Introduction to Variable and Feature Selection. Journal of Machine Learning Research 3: 1157-1182 (2003)

Jason Weston, André Elisseeff, Bernhard Schölkopf, Michael E. Tipping: Use of the Zero-Norm with Linear Models and Kernel Methods. Journal of Machine Learning Research 3: 1439-1461 (2003)

Jason Weston, Christina S. Leslie, Dengyong Zhou, André Elisseeff, William Stafford Noble: Semi-supervised Protein Classification Using Cluster Kernels. NIPS 2003

Olivier Bousquet, André Elisseeff: Stability and Generalization. Journal of Machine Learning Research 2: 499-526 (2002)

Olivier Bousquet, André Elisseeff: Stability and Generalization. Journal of Machine Learning Research 2: 499-526 (2002)

Savina Andonova, André Elisseeff, Theodoros Evgeniou, Massimiliano Pontil: A Simple Algorithm for Learning Stable Machines. ECAI 2002: 513-517

Jason Weston, Olivier Chapelle, André Elisseeff, Bernhard Schölkopf, Vladimir Vapnik: Kernel Dependency Estimation. NIPS 2002: 873-880

Asa Ben-Hur, André Elisseeff, Isabelle Guyon: A Stability Based Method for Discovering Structure in Clustered Data. Pacific Symposium on Biocomputing 2002: 6-17

Nello Cristianini, John Shawe-Taylor, André Elisseeff, Jaz S. Kandola: On Kernel-Target Alignment. NIPS 2001: 367-373

André Elisseeff, Jason Weston: A kernel method for multi-labelled classification. NIPS 2001: 681-687

Hélène Paugam-Moisy, André Elisseeff, Yann Guermeur: Generalization Performance of Multiclass Discriminant Models. IJCNN (4) 2000: 177-182

Yann Guermeur, André Elisseeff, Hélène Paugam-Moisy: A New Multi-Class SVM Based on a Uniform Convergence Result. IJCNN (4) 2000: 183-188

Olivier Bousquet, André Elisseeff: Algorithmic Stability and Generalization Performance. NIPS 2000: 196-202

André Elisseeff, Hélène Paugam-Moisy: JNN, a randomized algorithm for training multilayer networks in polynomial time. Neurocomputing 29(1-3): 3-24 (1999)

André Elisseeff, Hélène Paugam-Moisy: Size of Multilayer Networks for Exact Learning: Analytic Approach. NIPS 1996: 162-168