PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

EPrints submitted by Liva Ralaivola

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Number of EPrints submitted by this user: 21

Dynamical Modeling with Kernels for Nonlinear Time Series Prediction
Liva Ralaivola and Florence d'Alché-Buc
In: NIPS 2003, 9-11 Dec 2003, Vancouver, Canada.

Nonlinear Time series filtering, smoothing and learning using the kernel Kalman filter
Liva Ralaivola and Florence d'Alché-Buc
(2004) Technical Report. Universite Pierre et Marie Curie, Paris, France.

Graph kernels for chemical informatics
Liva Ralaivola, Sanjay J. Swamidass, Hiroto Saigo and Pierre Baldi
Neural Networks Volume 18, Number 8, pp. 1093-1110, 2005.

Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity
Joshua S. Swamidass, Jonathan Chen, Jocelyne Bruand, Peter Phung, Liva Ralaivola and Pierre Baldi
Bioinformatics Volume 21, Number suppl 1, pp. 359-368, 2005. ISSN 1460-2059

SVM and Pattern-Enriched Common Fate Graphs for the Game of Go
Liva Ralaivola, Lin Wu and Pierre Baldi
In: ESANN 2005, 27-29 Apr 2005, Bruges, Belgium.

Time Series Filtering, Smoothing and Learning using the Kernel Kalman Filter
Liva Ralaivola and Florence d'Alché-Buc
In: IJCNN 2005, 31 Jul- 4 Aug 2005, Montreal, Canada.

CN=CPCN
Liva Ralaivola, François Denis and Christophe N. Magnan
In: 2006, 25-29 June 2006, Pittsburgh, Pa, USA.

Efficient learning of naive Bayes classifiers under class-conditional classification noise
François Denis, Christophe N. Magnan and Liva Ralaivola
In: ICML 2006, 25-29 June 2006, Pittsburgh, Pa, USA.

The pharmacophore kernel for virtual screening with support vector machines
Pierre Mahé, Liva Ralaivola, Véronique Stoven and Jean-Philippe Vert
Journal of Chemical Information and Modeling Volume 46, Number 5, pp. 2003-2014, 2006.

New Methods in Machine Learning: Theory and Applications
François Denis and Liva Ralaivola, ed. (2006) RSTI - Revue d'Intelligence Artificielle , Volume 20 , Number 6. Lavoisier , Paris, France . ISBN 2 7462 1629 9

Learning Kernel Perceptron on Noisy Data using Random Projections
Guillaume Stempfel and Liva Ralaivola
In: 18th International Conference on Algorithmic Learning Theory, October 1 - 4, 2007, Sendai, Japan.

One- to Four-Dimensional Kernels for Virtual Screening and the Prediction of Physical, Chemical, and Biological Properties
Chloé Azencott, Alexandre Ksikes, Jonathan Chen, Joshua S. Swamidass, Liva Ralaivola and Pierre Baldi
J. Chemical Information and Modeling Volume 47, Number 3, pp. 965-975, 2007.

Learning from Noisy Data using Hyperplane Sampling and Sample Averages
Guillaume Stempfel, Liva Ralaivola and François Denis
(2007) Technical Report. HAL - CNRS, France.

Chromatic PAC-Bayes Bounds for Non-IID Data
Liva Ralaivola, Marie Szafranski and Guillaume Stempfel
In: AISTATS 09, 16-18 Apr 2009, Clearwater.

Semi-Supervised Bipartite Ranking with the Normalized Rayleigh Coefficient
Liva Ralaivola
In: ESANN 09, 23-25 Apr. 2009, Bruges.

Spring School in Machine Learning and Teaching experiences
Cécile Capponi, François Denis, Rémi Eyraud, Amaury Habrard and Liva Ralaivola
In: TML Workshop 2008, 3-5 May 2008, Saint-Etienne.

Grammatical Inference as a Principal Component Analysis Problem
Raphael Bailly, François Denis and Liva Ralaivola
In: ICML 09, 14-18 June 2009, Montreal, Canada.

Multiple Indefinite Kernel Learning with Mixed Norm Regularization
Matthieu Kowalski, Marie Szafranski and Liva Ralaivola
In: ICML 09, 14-18 June 2009, Montreal, Canada.

Learning SVMs from Sloppily Labeled Data
Guillaume Stempfel and Liva Ralaivola
In: ICANN 09, 14-17 Sept 2009, Cyprus.

Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary β-Mixing Processes
Liva Ralaivola, Marie Szafranski and Guillaume Stempfel
Journal of Machine Learning Research Volume 11, pp. 1927-1956, 2010.

MKPM: a Multiclass extension of the Kernel Projection Machine
Sylvain Takerkart and Liva Ralaivola
In: CVPR 2011, 20-25 Jun 2011, Colorado Springs, USA.