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Liva Ralaivola
2010 – today
- 2012
[c19]Emilie Morvant, Sokol Koço, Liva Ralaivola: PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class Classification. ICML 2012- [c18]Sylvain Takerkart, Guillaume Auzias, Bertrand Thirion, Daniele Schön, Liva Ralaivola: Graph-Based Inter-subject Classification of Local fMRI Patterns. MLMI 2012: 184-192
- [c17]Liva Ralaivola: Confusion-Based Online Learning and a Passive-Aggressive Scheme. NIPS 2012: 3293-3301
- [i4]Pierre Machart, Thomas Peel, Liva Ralaivola, Sandrine Anthoine, Hervé Glotin: Stochastic Low-Rank Kernel Learning for Regression. CoRR abs/1201.2416 (2012)
- [i3]Pierre Machart, Liva Ralaivola: Confusion Matrix Stability Bounds for Multiclass Classification. CoRR abs/1202.6221 (2012)
- [i2]Emilie Morvant, Sokol Koço, Liva Ralaivola: PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class Classification. CoRR abs/1202.6228 (2012)
- 2011
- [c16]Liva Ralaivola, Benoît Favre, Pierre Gotab, Frédéric Béchet, Géraldine Damnati: Applying Multiclass Bandit algorithms to call-type classification. ASRU 2011: 431-436
- [c15]Sylvain Takerkart, Liva Ralaivola: MKPM: A multiclass extension to the kernel projection machine. CVPR 2011: 2785-2791
- [c14]Pierre Machart, Thomas Peel, Sandrine Anthoine, Liva Ralaivola, Hervé Glotin: Stochastic Low-Rank Kernel Learning for Regression. ICML 2011: 969-976
- 2010
- [j3]Liva Ralaivola, Marie Szafranski, Guillaume Stempfel: Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary β-Mixing Processes. Journal of Machine Learning Research 11: 1927-1956 (2010)
- [c13]Thomas Peel, Sandrine Anthoine, Liva Ralaivola: Empirical Bernstein Inequalities for U-Statistics. NIPS 2010: 1903-1911
2000 – 2009
- 2009
- [j2]Liva Ralaivola, Marie Szafranski, Guillaume Stempfel: Chromatic PAC-Bayes Bounds for Non-IID Data. Journal of Machine Learning Research - Proceedings Track 5: 416-423 (2009)
- [c12]Liva Ralaivola: Semi-supervised bipartite ranking with the normalized Rayleigh coefficient. ESANN 2009
- [c11]Guillaume Stempfel, Liva Ralaivola: Learning SVMs from Sloppily Labeled Data. ICANN (1) 2009: 884-893
- [c10]Raphaël Bailly, François Denis, Liva Ralaivola: Grammatical inference as a principal component analysis problem. ICML 2009: 5
- [c9]Matthieu Kowalski, Marie Szafranski, Liva Ralaivola: Multiple indefinite kernel learning with mixed norm regularization. ICML 2009: 69
- [i1]Liva Ralaivola, Marie Szafranski, Guillaume Stempfel: Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary \beta-Mixing Processes. CoRR abs/0909.1933 (2009)
- 2007
- [c8]Guillaume Stempfel, Liva Ralaivola: Learning Kernel Perceptrons on Noisy Data Using Random Projections. ALT 2007: 328-342
- 2006
- [c7]François Denis, Christophe Nicolas Magnan, Liva Ralaivola: Efficient learning of Naive Bayes classifiers under class-conditional classification noise. ICML 2006: 265-272
- [c6]
- 2005
- [j1]Liva Ralaivola, Sanjay Joshua Swamidass, Hiroto Saigo, Pierre Baldi: Graph kernels for chemical informatics. Neural Networks 18(8): 1093-1110 (2005)
- [c5]Liva Ralaivola, Lin Wu, Pierre Baldi: SVM and pattern-enriched common fate graphs for the game of go. ESANN 2005: 485-490
- [c4]Sanjay Joshua Swamidass, Jonathan H. Chen, Jocelyne Bruand, Peter Phung, Liva Ralaivola, Pierre Baldi: Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity. ISMB (Supplement of Bioinformatics) 2005: 359-368
- 2003
- [c3]Bruno-Edouard Perrin, Liva Ralaivola, Aurélien Mazurie, Samuele Bottani, Jacques Mallet, Florence d'Alché-Buc: Gene networks inference using dynamic Bayesian networks. ECCB 2003: 138-148
- [c2]Liva Ralaivola, Florence d'Alché-Buc: Dynamical Modeling with Kernels for Nonlinear Time Series Prediction. NIPS 2003
- 2001
- [c1]Liva Ralaivola, Florence d'Alché-Buc: Incremental Support Vector Machine Learning: A Local Approach. ICANN 2001: 322-330
Coauthor Index
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last updated on 2013-02-25 18:36 CET by the dblp team