L. Ralaivola

发表

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

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

Gene networks inference using dynamic Bayesian networks

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

Confusion Matrix Stability Bounds for Multiclass Classification pdf

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

Empirical Bernstein Inequality for Martingales : Application to Online Learning

L. Ralaivola, Thomas Peel, S. Anthoine, 2013 .

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

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

N ov 2 01 8 Dependency-dependent Bounds for Sums of Dependent Random Variables

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

Dependency-dependent Bounds for Sums of Dependent Random Variables

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

Bernoulli Dependency-dependent Bounds for Sums of Dependent Random Variables

Christoph H. Lampert, L. Ralaivola, Alexander Zimin, 2017 .

Learning SVMs from Sloppily Labeled Data

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

Integrating knowledge graph embeddings to improve mention representation for bridging anaphora resolution

L. Ralaivola, O. Pandit, Pascal Denis, 2020, CRAC.

Graph-Based Inter-Subject Pattern Analysis of fMRI Data

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

On the Generalization of the C-Bound to Multiclass Setting

François Laviolette, Emilie Morvant, L. Ralaivola, 2014 .

Apprentissage de classifieurs naı̈fs de Bayes à partir de données soumises à un bruit de classification conditionnel à chaque classe

F. Denis, L. Ralaivola, C. Magnan, 2022 .

A dynamic model of gene regulatory networks based on inertia principle

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

Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances

A. Rakotomamonjy, L. Ralaivola, Kimia Nadjahi, 2022, ArXiv.

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

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

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 .

Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances

A. Rakotomamonjy, L. Ralaivola, Kimia Nadjahi, 2022, ArXiv.

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

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

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

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

Learning Kernel Perceptrons on Noisy Data Using Random Projections

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

Learning from Noisy Data using Hyperplane Sampling and Sample Averages

Liva Ralaivola, François Denis, Guillaume Stempfel, 2007 .

CN = CPCN

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

Graph kernels for chemical informatics

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

On Binary Reduction of Large-Scale Multiclass Classification Problems

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

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

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

Semi-supervised bipartite ranking with the normalized Rayleigh coefficient

Liva Ralaivola, L. Ralaivola, 2009, ESANN.

Entropy-Based Concentration Inequalities for Dependent Variables

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

Chromatic PAC-Bayes Bounds for Non-IID Data

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

The Pharmacophore Kernel for Virtual Screening with Support Vector Machines

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

Confusion-Based Online Learning and a Passive-Aggressive Scheme

Liva Ralaivola, L. Ralaivola, 2012, NIPS.

Graph-Based Inter-subject Classification of Local fMRI Patterns

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

Incremental Support Vector Machine Learning: A Local Approach

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

Personalised Federated Learning On Heterogeneous Feature Spaces

A. Rakotomamonjy, L. Ralaivola, Maxime Vono, 2023, ArXiv.

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..

Time series filtering, smoothing and learning using the kernel Kalman filter

L. Ralaivola, F. d'Alche-Buc, L. Ralaivola, 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

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

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

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

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

A dynamic screening principle for the Lasso

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

Photonic Differential Privacy with Direct Feedback Alignment

A. Rakotomamonjy, L. Ralaivola, Julien Launay, 2021, NeurIPS.

Partial Trace Regression and Low-Rank Kraus Decomposition

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

Quantum bandits

H. Kadri, L. Ralaivola, G. Di Molfetta, 2020, Quantum Machine Intelligence.

Quantum bandits

H. Kadri, L. Ralaivola, G. Molfetta, 2020, Quantum Machine Intelligence.

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.

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.

Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary β-Mixing Processes

L. Ralaivola, RalaivolaLiva, SzafranskiMarie, 2010 .

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.

Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances

A. Rakotomamonjy, L. Ralaivola, Kimia Nadjahi, 2022, ICML.

Grammatical inference as a principal component analysis problem

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

Dissimilarity Measure Machines

Nicolas Courty, Alain Rakotomamonjy, Rémi Flamary, 2018 .

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

Valentin Emiya, Liva Ralaivola, Antoine Bonnefoy, 2013 .

Bandits Dueling on Partially Ordered Sets

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

Unconfused ultraconservative multiclass algorithms

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

The Pharmacophore Kernel for Virtual Screening with Support Vector Machines

Jean-Philippe Vert, L. Ralaivola, V. Stoven, 2006, Journal of Chemical Information and Modeling.

Dynamical Modeling with Kernels for Nonlinear Time Series Prediction

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

Differentially Private Sliced Wasserstein Distance pdf

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

More efficient sparsity-inducing algorithms using inexact gradient

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

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

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

Stochastic Low-Rank Kernel Learning for Regression pdf

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

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

A. Rakotomamonjy, L. Ralaivola, Sokol Koço, 2017, IEEE Transactions on Neural Networks and Learning Systems.

On the Generalization of the C-Bound to Structured Output Ensemble Methods

Emilie Morvant, L. Ralaivola, Franccois Laviolette, 2015 .