Liva Ralaivola

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Dissimilarity Measure Machines

Nicolas Courty, Alain Rakotomamonjy, Rémi Flamary, 2018 .

Confusion Matrix Stability Bounds for Multiclass Classification pdf

Liva Ralaivola, Pierre Machart, Pierre Machart, 2012, ArXiv.

A dynamic screening principle for the Lasso

Rémi Gribonval, Valentin Emiya, Liva Ralaivola, 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).

Online multiclass learning with "bandit" feedback under a Passive-Aggressive approach

Emmanuel Daucé, Liva Ralaivola, Hongliang Zhong, 2015, ESANN.

The Pharmacophore Kernel for Virtual Screening with Support Vector Machines

Jean-Philippe Vert, Véronique Stoven, Liva Ralaivola, 2006, J. Chem. Inf. Model..

Unconfused ultraconservative multiclass algorithms

Liva Ralaivola, Ugo Louche, L. Ralaivola, 2013, Machine Learning.

Grammatical inference as a principal component analysis problem

Liva Ralaivola, François Denis, Raphaël Bailly, 2009, ICML '09.

On Generalizing the C-Bound to the Multiclass and Multi-label Settings pdf

François Laviolette, Emilie Morvant, Liva Ralaivola, 2015, ArXiv.

Incremental Learning Algorithms for Classification and Regression: local strategies

Liva Ralaivola, Florence d'Alché-Buc, 2002 .

Graph-Based Inter-subject Classification of Local fMRI Patterns

Bertrand Thirion, Liva Ralaivola, Guillaume Auzias, 2012, MLMI.

Matching pursuit with stochastic selection

Valentin Emiya, Liva Ralaivola, Thomas Peel, 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

Entropy-Based Concentration Inequalities for Dependent Variables

Massih-Reza Amini, Liva Ralaivola, Massih-Reza Amini, 2015, ICML.

CN = CPCN

Liva Ralaivola, François Denis, Christophe Nicolas Magnan, 2006, ICML.

More efficient sparsity-inducing algorithms using inexact gradient

Alain Rakotomamonjy, Liva Ralaivola, Sokol Koço, 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

Confusion-Based Online Learning and a Passive-Aggressive Scheme

Liva Ralaivola, L. Ralaivola, 2012, NIPS.

QuicK-means: Acceleration of K-means by learning a fast transform pdf

Valentin Emiya, Liva Ralaivola, Hachem Kadri, 2019, ArXiv.

Multiple indefinite kernel learning with mixed norm regularization

Liva Ralaivola, Matthieu Kowalski, Marie Szafranski, 2009, ICML '09.

PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class Classification pdf

Emilie Morvant, Liva Ralaivola, Sokol Koço, 2012, ICML.

Chromatic PAC-Bayes Bounds for Non-IID Data

Liva Ralaivola, Marie Szafranski, Guillaume Stempfel, 2009, AISTATS.

Incremental Support Vector Machine Learning: A Local Approach

Liva Ralaivola, Florence d'Alché-Buc, L. Ralaivola, 2001, ICANN.

Recovery and Convergence Rate of the Frank–Wolfe Algorithm for the m-Exact-Sparse Problem

Valentin Emiya, Liva Ralaivola, Sandrine Anthoine, 2019, IEEE Transactions on Information Theory.

Fast online adaptivity with policy gradient: example of the BCI "P300"-speller

Emmanuel Daucé, Liva Ralaivola, Timothée Proix, 2013, ESANN.

Dynamic Screening: Accelerating First-Order Algorithms for the Lasso and Group-Lasso

Rémi Gribonval, Valentin Emiya, Liva Ralaivola, 2014, IEEE Transactions on Signal Processing.

Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity

Pierre Baldi, Sanjay Joshua Swamidass, Liva Ralaivola, 2005, ISMB.

Quantum bandits

Liva Ralaivola, Hachem Kadri, Giuseppe Di Molfetta, 2020, Quantum Mach. Intell..

From cutting planes algorithms to compression schemes and active learning

Liva Ralaivola, Ugo Louche, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

Learning Kernel Perceptrons on Noisy Data Using Random Projections

Liva Ralaivola, Guillaume Stempfel, L. Ralaivola, 2007, ALT.

Empirical Bernstein Inequalities for U-Statistics

Liva Ralaivola, Thomas Peel, Sandrine Anthoine, 2010, NIPS.

One- to Four-Dimensional Kernels for Virtual Screening and the Prediction of Physical, Chemical, and Biological Properties

Pierre Baldi, Sanjay Joshua Swamidass, Liva Ralaivola, 2007, J. Chem. Inf. Model..

Cornering Stationary and Restless Mixing Bandits with Remix-UCB

Julien Audiffren, Liva Ralaivola, 2015, NIPS.

A dynamic model of gene regulatory networks based on inertia principle

Liva Ralaivola, Pierre-Jean Lahaye, Florence d’Alché–Buc, 2005 .

Fast Solving of the Group-Lasso via Dynamic Screening

Valentin Emiya, Liva Ralaivola, Antoine Bonnefoy, 2015 .

Multi-class to Binary reduction of Large-scale classification Problems

Bikash Joshi, Ioannis Partalas, Massih-Reza Amini, 2015 .

S-ISTA and Brothers : a Dynamic Screening test principle for the Lasso

Valentin Emiya, Liva Ralaivola, Antoine Bonnefoy, 2013 .

Dynamical Modeling with Kernels for Nonlinear Time Series Prediction

Liva Ralaivola, Florence d'Alché-Buc, L. Ralaivola, 2003, NIPS.

Stochastic Low-Rank Kernel Learning for Regression pdf

Hervé Glotin, Liva Ralaivola, Pierre Machart, 2011, ICML.

Online Learning of Task-specific Word Representations with a Joint Biconvex Passive-Aggressive Algorithm

Pascal Denis, Liva Ralaivola, 2017, EACL.

Reward-based online learning in non-stationary environments: Adapting a P300-speller with a “backspace” key

Emmanuel Daucé, Liva Ralaivola, Timothée Proix, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

Efficient learning of Naive Bayes classifiers under class-conditional classification noise

Liva Ralaivola, François Denis, Christophe Nicolas Magnan, 2006, ICML.

Partial Trace Regression and Low-Rank Kraus Decomposition

Liva Ralaivola, Hachem Kadri, Alain Rakotomamonjy, 2020, ICML.

Bandits Dueling on Partially Ordered Sets

Julien Audiffren, Liva Ralaivola, J. Audiffren, 2017, NIPS.

Multiple subject learning for inter-subject prediction

Liva Ralaivola, Sylvain Takerkart, 2014, 2014 International Workshop on Pattern Recognition in Neuroimaging.

Greedy Methods, Randomization Approaches, and Multiarm Bandit Algorithms for Efficient Sparsity-Constrained Optimization

Liva Ralaivola, Alain Rakotomamonjy, Sokol Koço, 2015, IEEE Transactions on Neural Networks and Learning Systems.

Semi-supervised bipartite ranking with the normalized Rayleigh coefficient

Liva Ralaivola, L. Ralaivola, 2009, ESANN.

Frank-Wolfe Algorithm for the Exact Sparse Problem pdf

Valentin Emiya, Liva Ralaivola, Sandrine Anthoine, 2018, ArXiv.

Greedy methods, randomization approaches and multi-arm bandit algorithms for efficient sparsity-constrained optimization

Alain Rakotomamonjy, Liva Ralaivola, Sokol Koço, 2015, IEEE transactions on neural networks and learning systems.

SVM and pattern-enriched common fate graphs for the game of go

Lin Wu, Pierre Baldi, Liva Ralaivola, 2005, ESANN.

Stationary Mixing Bandits pdf

Julien Audiffren, Liva Ralaivola, 2014, ArXiv.

Learning Rich Event Representations and Interactions for Temporal Relation Classification

Liva Ralaivola, Onkar Pandit, Pascal Denis, 2019, ESANN.

Learning from Noisy Data using Hyperplane Sampling and Sample Averages

Liva Ralaivola, François Denis, Guillaume Stempfel, 2007 .

Learning SVMs from Sloppily Labeled Data

Liva Ralaivola, Guillaume Stempfel, L. Ralaivola, 2009, ICANN.

Gene networks inference using dynamic Bayesian networks

Aurélien Mazurie, Liva Ralaivola, Florence d'Alché-Buc, 2003, ECCB.

On Binary Reduction of Large-Scale Multiclass Classification Problems

Bikash Joshi, Ioannis Partalas, Massih-Reza Amini, 2015, IDA.

Applying Multiclass Bandit algorithms to call-type classification

Frédéric Béchet, Benoît Favre, Géraldine Damnati, 2011, 2011 IEEE Workshop on Automatic Speech Recognition & Understanding.

MKPM: A multiclass extension to the kernel projection machine

Liva Ralaivola, Sylvain Takerkart, 2011, CVPR 2011.

Graph kernels for chemical informatics

Pierre Baldi, Sanjay Joshua Swamidass, Hiroto Saigo, 2005, Neural Networks.

Risk upper bounds for general ensemble methods with an application to multiclass classification

François Laviolette, Emilie Morvant, Liva Ralaivola, 2017, Neurocomputing.

Indistinguishable Bandits Dueling with Decoys on a Poset pdf

Julien Audiffren, Liva Ralaivola, 2016, ArXiv.

Photonic Differential Privacy with Direct Feedback Alignment pdf

Ruben Ohana, Alain Rakotomamonjy, Liva Ralaivola, 2021, ArXiv.

Differentially Private Sliced Wasserstein Distance pdf

Alain Rakotomamonjy, Liva Ralaivola, A. Rakotomamonjy, 2021, ICML.

QuicK-means: accelerating inference for K-means by learning fast transforms

Valentin Emiya, Liva Ralaivola, Hachem Kadri, 2021, Machine Learning.

One- to Four-Dimensional Kernels for Virtual Screening and the Prediction of Physical, Chemical, and Biological Properties.

Pierre Baldi, Liva Ralaivola, S. Joshua Swamidass, 2007 .

Graph-Based Inter-Subject Pattern Analysis of fMRI Data

Bertrand Thirion, Liva Ralaivola, Guillaume Auzias, 2014, PloS one.

Learning Kernel Perceptrons on Noisy Data and Random Projections

Liva Ralaivola, Guillaume Stempfel, 2017 .

Dependency-dependent Bounds for Sums of Dependent Random Variables

Christoph H. Lampert, Liva Ralaivola, Alexander Zimin, 2018 .