| 2024 | ICLR | Federated Wasserstein Distance. | Alain Rakotomamonjy, Kimia Nadjahi, Liva Ralaivola |
| 2023 | ICML | Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances. | Ruben Ohana, Kimia Nadjahi, Alain Rakotomamonjy, Liva Ralaivola |
| 2022 | ICASSP | Scalable Ridge Leverage Score Sampling for the Nystrm Method. | Farah Cherfaoui, Hachem Kadri, Liva Ralaivola |
| 2021 | ICML | Differentially Private Sliced Wasserstein Distance. | Alain Rakotomamonjy, Liva Ralaivola |
| 2020 | ICML | Partial Trace Regression and Low-Rank Kraus Decomposition. | Hachem Kadri, Stphane Ayache, Riikka Huusari, Alain Rakotomamonjy, Liva Ralaivola |
| 2019 | ESANN | Learning Rich Event Representations and Interactions for Temporal Relation Classification. | Onkar Arun Pandit, Pascal Denis, Liva Ralaivola |
| 2017 | EACL | Online Learning of Task-specific Word Representations with a Joint Biconvex Passive-Aggressive Algorithm. | Pascal Denis, Liva Ralaivola |
| 2015 | ESANN | Online multiclass learning with "bandit" feedback under a Passive-Aggressive approach. | Hongliang Zhong, Emmanuel Dauc, Liva Ralaivola |
| 2015 | ICML | Entropy-Based Concentration Inequalities for Dependent Variables. | Liva Ralaivola, Massih-Reza Amini |
| 2015 | IJCNN | Reward-based online learning in non-stationary environments: Adapting a P300-speller with a "backspace" key. | Emmanuel Dauc, Timothe Proix, Liva Ralaivola |
| 2015 | IJCNN | From cutting planes algorithms to compression schemes and active learning. | Ugo Louche, Liva Ralaivola |
| 2015 | IDA | On Binary Reduction of Large-Scale Multiclass Classification Problems. | Bikash Joshi, Massih-Reza Amini, Ioannis Partalas, Liva Ralaivola, Nicolas Usunier, ric Gaussier |
| 2013 | ACML | Unconfused Ultraconservative Multiclass Algorithms. | Ugo Louche, Liva Ralaivola |
| 2013 | ESANN | Fast online adaptivity with policy gradient: example of the BCI "P300"-speller. | Emmanuel Dauc, Timothe Proix, Liva Ralaivola |
| 2012 | ICML | PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class Classification. | Emilie Morvant, Sokol Koo, Liva Ralaivola |
| 2012 | MICCAI | Graph-Based Inter-subject Classification of Local fMRI Patterns. | Sylvain Takerkart, Guillaume Auzias, Bertrand Thirion, Daniele Schn, Liva Ralaivola |
| 2011 | ASRU | Applying Multiclass Bandit algorithms to call-type classification. | Liva Ralaivola, Benot Favre, Pierre Gotab, Frdric Bchet, Graldine Damnati |
| 2011 | CVPR | MKPM: A multiclass extension to the kernel projection machine. | Sylvain Takerkart, Liva Ralaivola |
| 2011 | ICML | Stochastic Low-Rank Kernel Learning for Regression. | Pierre Machart, Thomas Peel, Sandrine Anthoine, Liva Ralaivola, Herv Glotin |
| 2009 | ESANN | Semi-supervised bipartite ranking with the normalized Rayleigh coefficient. | Liva Ralaivola |
| 2009 | ICANN | Learning SVMs from Sloppily Labeled Data. | Guillaume Stempfel, Liva Ralaivola |
| 2009 | ICML | Grammatical inference as a principal component analysis problem. | Raphal Bailly, Franois Denis, Liva Ralaivola |
| 2009 | ICML | Multiple indefinite kernel learning with mixed norm regularization. | Matthieu Kowalski, Marie Szafranski, Liva Ralaivola |
| 2007 | ALT | Learning Kernel Perceptrons on Noisy Data Using Random Projections. | Guillaume Stempfel, Liva Ralaivola |
| 2006 | ICML | Efficient learning of Naive Bayes classifiers under class-conditional classification noise. | Franois Denis, Christophe Nicolas Magnan, Liva Ralaivola |
| 2006 | ICML | CN = CPCN. | Liva Ralaivola, Franois Denis, Christophe Nicolas Magnan |
| 2005 | ESANN | SVM and pattern-enriched common fate graphs for the game of go. | Liva Ralaivola, Lin Wu, Pierre Baldi |
| 2005 | IJCNN | Time series filtering, smoothing and learning using the kernel Kalman filter. | Liva Ralaivola, Florence d'Alch-Buc |
| 2005 | ISMB | Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity. | Sanjay Joshua Swamidass, Jonathan H. Chen, Jocelyne Bruand, Peter Phung, Liva Ralaivola, Pierre Baldi |
| 2003 | ECCB | Gene networks inference using dynamic Bayesian networks. | Bruno-Edouard Perrin, Liva Ralaivola, Aurlien Mazurie, Samuele Bottani, Jacques Mallet, Florence d'Alch-Buc |
| 2001 | ICANN | Incremental Support Vector Machine Learning: A Local Approach. | Liva Ralaivola, Florence d'Alch-Buc |