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2020 – today
- 2024
[j12]Alain Rakotomamonjy, Maxime Vono, Hamlet Jesse Medina Ruiz, Liva Ralaivola:
Personalised Federated Learning On Heterogeneous Feature Spaces. Trans. Mach. Learn. Res. 2024 (2024)- [c41]Alain Rakotomamonjy, Kimia Nadjahi, Liva Ralaivola:
Federated Wasserstein Distance. ICLR 2024 - 2023
- [c40]Ruben Ohana, Kimia Nadjahi, Alain Rakotomamonjy, Liva Ralaivola:
Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances. ICML 2023: 26451-26473 - [i21]Alain Rakotomamonjy, Maxime Vono, Hamlet Jesse Medina Ruiz, Liva Ralaivola:
Personalised Federated Learning On Heterogeneous Feature Spaces. CoRR abs/2301.11447 (2023) - [i20]Alain Rakotomamonjy, Kimia Nadjahi, Liva Ralaivola:
Federated Wasserstein Distance. CoRR abs/2310.01973 (2023) - 2022
- [c39]Farah Cherfaoui, Hachem Kadri, Liva Ralaivola:
Scalable Ridge Leverage Score Sampling for the Nyström Method. ICASSP 2022: 4163-4167 - [i19]Ruben Ohana, Kimia Nadjahi, Alain Rakotomamonjy, Liva Ralaivola:
Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances. CoRR abs/2206.03230 (2022) - 2021
- [j11]Luc Giffon
, Valentin Emiya, Hachem Kadri, Liva Ralaivola:
QuicK-means: accelerating inference for K-means by learning fast transforms. Mach. Learn. 110(5): 881-905 (2021) - [c38]Alain Rakotomamonjy, Liva Ralaivola:
Differentially Private Sliced Wasserstein Distance. ICML 2021: 8810-8820 - [c37]Ruben Ohana, Hamlet Jesse Medina Ruiz, Julien Launay, Alessandro Cappelli, Iacopo Poli, Liva Ralaivola, Alain Rakotomamonjy:
Photonic Differential Privacy with Direct Feedback Alignment. NeurIPS 2021: 22010-22020 - [i18]Ruben Ohana, Hamlet Jesse Medina Ruiz, Julien Launay, Alessandro Cappelli, Iacopo Poli, Liva Ralaivola, Alain Rakotomamonjy:
Photonic Differential Privacy with Direct Feedback Alignment. CoRR abs/2106.03645 (2021) - [i17]Alain Rakotomamonjy, Liva Ralaivola:
Differentially Private Sliced Wasserstein Distance. CoRR abs/2107.01848 (2021) - 2020
- [j10]Balthazar Casalé, Giuseppe Di Molfetta, Hachem Kadri, Liva Ralaivola:
Quantum bandits. Quantum Mach. Intell. 2(1): 1-7 (2020) - [c36]Hachem Kadri, Stéphane Ayache, Riikka Huusari, Alain Rakotomamonjy, Liva Ralaivola:
Partial Trace Regression and Low-Rank Kraus Decomposition. ICML 2020: 5031-5041 - [i16]Balthazar Casalé, Giuseppe Di Molfetta, Hachem Kadri, Liva Ralaivola:
Quantum Bandits. CoRR abs/2002.06395 (2020) - [i15]Hachem Kadri, Stéphane Ayache, Riikka Huusari, Alain Rakotomamonjy, Liva Ralaivola:
Partial Trace Regression and Low-Rank Kraus Decomposition. CoRR abs/2007.00935 (2020)
2010 – 2019
- 2019
- [j9]Farah Cherfaoui, Valentin Emiya, Liva Ralaivola, Sandrine Anthoine:
Recovery and Convergence Rate of the Frank-Wolfe Algorithm for the m-Exact-Sparse Problem. IEEE Trans. Inf. Theory 65(11): 7407-7414 (2019) - [c35]Onkar Arun Pandit, Pascal Denis, Liva Ralaivola:
Learning Rich Event Representations and Interactions for Temporal Relation Classification. ESANN 2019 - [i14]Luc Giffon, Valentin Emiya, Liva Ralaivola, Hachem Kadri:
QuicK-means: Acceleration of K-means by learning a fast transform. CoRR abs/1908.08713 (2019) - 2018
- [i13]Farah Cherfaoui, Valentin Emiya, Liva Ralaivola, Sandrine Anthoine:
Frank-Wolfe Algorithm for the Exact Sparse Problem. CoRR abs/1812.07201 (2018) - 2017
- [j8]François Laviolette, Emilie Morvant, Liva Ralaivola, Jean-Francis Roy:
Risk upper bounds for general ensemble methods with an application to multiclass classification. Neurocomputing 219: 15-25 (2017) - [j7]Alain Rakotomamonjy, Sokol Koço, Liva Ralaivola:
Greedy Methods, Randomization Approaches, and Multiarm Bandit Algorithms for Efficient Sparsity-Constrained Optimization. IEEE Trans. Neural Networks Learn. Syst. 28(11): 2789-2802 (2017) - [c34]Pascal Denis, Liva Ralaivola:
Online Learning of Task-specific Word Representations with a Joint Biconvex Passive-Aggressive Algorithm. EACL (1) 2017: 775-784 - [c33]Julien Audiffren, Liva Ralaivola:
Bandits Dueling on Partially Ordered Sets. NIPS 2017: 2129-2138 - 2016
- [i12]Julien Audiffren, Liva Ralaivola:
Indistinguishable Bandits Dueling with Decoys on a Poset. CoRR abs/1602.02706 (2016) - 2015
- [j6]Ugo Louche, Liva Ralaivola:
Unconfused ultraconservative multiclass algorithms. Mach. Learn. 99(2): 327-351 (2015) - [j5]Antoine Bonnefoy, Valentin Emiya, Liva Ralaivola, Rémi Gribonval:
Dynamic Screening: Accelerating First-Order Algorithms for the Lasso and Group-Lasso. IEEE Trans. Signal Process. 63(19): 5121-5132 (2015) - [c32]Hongliang Zhong, Emmanuel Daucé, Liva Ralaivola:
Online multiclass learning with "bandit" feedback under a Passive-Aggressive approach. ESANN 2015 - [c31]Alain Rakotomamonjy, Sokol Koço, Liva Ralaivola:
More efficient sparsity-inducing algorithms using inexact gradient. EUSIPCO 2015: 709-713 - [c30]Liva Ralaivola, Massih-Reza Amini:
Entropy-Based Concentration Inequalities for Dependent Variables. ICML 2015: 2436-2444 - [c29]Bikash Joshi, Massih-Reza Amini, Ioannis Partalas, Liva Ralaivola, Nicolas Usunier, Éric Gaussier:
On Binary Reduction of Large-Scale Multiclass Classification Problems. IDA 2015: 132-144 - [c28]Emmanuel Daucé, Timothée Proix, Liva Ralaivola:
Reward-based online learning in non-stationary environments: Adapting a P300-speller with a "backspace" key. IJCNN 2015: 1-8 - [c27]Ugo Louche, Liva Ralaivola:
From cutting planes algorithms to compression schemes and active learning. IJCNN 2015: 1-8 - [c26]Julien Audiffren, Liva Ralaivola:
Cornering Stationary and Restless Mixing Bandits with Remix-UCB. NIPS 2015: 3339-3347 - [i11]François Laviolette, Emilie Morvant, Liva Ralaivola, Jean-Francis Roy:
On Generalizing the C-Bound to the Multiclass and Multi-label Settings. CoRR abs/1501.03001 (2015) - [i10]Ugo Louche, Liva Ralaivola:
Unconfused ultraconservative multiclass algorithms. CoRR abs/1506.07254 (2015) - [i9]Liva Ralaivola, Ugo Louche:
From Cutting Planes Algorithms to Compression Schemes and Active Learning. CoRR abs/1508.02986 (2015) - [i8]Alain Rakotomamonjy, Sokol Koço, Liva Ralaivola:
Greedy methods, randomization approaches and multi-arm bandit algorithms for efficient sparsity-constrained optimization. CoRR abs/1508.06477 (2015) - 2014
- [c25]Antoine Bonnefoy, Valentin Emiya, Liva Ralaivola, Rémi Gribonval:
A dynamic screening principle for the Lasso. EUSIPCO 2014: 6-10 - [c24]Sylvain Takerkart, Liva Ralaivola:
Multiple subject learning for inter-subject prediction. PRNI 2014: 1-4 - [i7]Ugo Louche, Liva Ralaivola:
Unconfused Ultraconservative Multiclass Algorithms. CoRR abs/1403.5115 (2014) - [i6]Julien Audiffren, Liva Ralaivola:
Stationary Mixing Bandits. CoRR abs/1406.6020 (2014) - [i5]Antoine Bonnefoy, Valentin Emiya, Liva Ralaivola, Rémi Gribonval:
Dynamic Screening: Accelerating First-Order Algorithms for the Lasso and Group-Lasso. CoRR abs/1412.4080 (2014) - 2013
- [c23]Ugo Louche, Liva Ralaivola:
Unconfused Ultraconservative Multiclass Algorithms. ACML 2013: 309-324 - [c22]Emmanuel Daucé, Timothée Proix, Liva Ralaivola:
Fast online adaptivity with policy gradient: example of the BCI "P300"-speller. ESANN 2013 - 2012
- [c21]Thomas Peel, Valentin Emiya, Liva Ralaivola, Sandrine Anthoine:
Matching pursuit with stochastic selection. EUSIPCO 2012: 879-883 - [c20]Emilie Morvant, Sokol Koço, Liva Ralaivola:
PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class Classification. ICML 2012 - [c19]Sylvain Takerkart, Guillaume Auzias, Bertrand Thirion, Daniele Schön, Liva Ralaivola:
Graph-Based Inter-subject Classification of Local fMRI Patterns. MLMI 2012: 184-192 - [c18]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
- [c17]Liva Ralaivola, Benoît Favre, Pierre Gotab, Frédéric Béchet, Géraldine Damnati:
Applying Multiclass Bandit algorithms to call-type classification. ASRU 2011: 431-436 - [c16]Sylvain Takerkart, Liva Ralaivola:
MKPM: A multiclass extension to the kernel projection machine. CVPR 2011: 2785-2791 - [c15]Pierre Machart, Thomas Peel, Sandrine Anthoine, Liva Ralaivola, Hervé Glotin:
Stochastic Low-Rank Kernel Learning for Regression. ICML 2011: 969-976 - 2010
- [j4]Liva Ralaivola, Marie Szafranski, Guillaume Stempfel:
Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary β-Mixing Processes. J. Mach. Learn. Res. 11: 1927-1956 (2010) - [c14]Thomas Peel, Sandrine Anthoine, Liva Ralaivola:
Empirical Bernstein Inequalities for U-Statistics. NIPS 2010: 1903-1911
2000 – 2009
- 2009
- [c13]Liva Ralaivola:
Semi-supervised bipartite ranking with the normalized Rayleigh coefficient. ESANN 2009 - [c12]Guillaume Stempfel, Liva Ralaivola:
Learning SVMs from Sloppily Labeled Data. ICANN (1) 2009: 884-893 - [c11]Raphaël Bailly, François Denis, Liva Ralaivola:
Grammatical inference as a principal component analysis problem. ICML 2009: 33-40 - [c10]Matthieu Kowalski, Marie Szafranski, Liva Ralaivola:
Multiple indefinite kernel learning with mixed norm regularization. ICML 2009: 545-552 - [c9]Liva Ralaivola, Marie Szafranski, Guillaume Stempfel:
Chromatic PAC-Bayes Bounds for Non-IID Data. AISTATS 2009: 416-423 - [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
- [j3]Chloé-Agathe Azencott, Alexandre Ksikes, S. Joshua Swamidass, Jonathan H. Chen, Liva Ralaivola, Pierre Baldi:
One- to Four-Dimensional Kernels for Virtual Screening and the Prediction of Physical, Chemical, and Biological Properties. J. Chem. Inf. Model. 47(3): 965-974 (2007) - [c8]Guillaume Stempfel, Liva Ralaivola:
Learning Kernel Perceptrons on Noisy Data Using Random Projections. ALT 2007: 328-342 - 2006
- [j2]Pierre Mahé, Liva Ralaivola, Véronique Stoven, Jean-Philippe Vert:
The Pharmacophore Kernel for Virtual Screening with Support Vector Machines. J. Chem. Inf. Model. 46(5): 2003-2014 (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]Liva Ralaivola, François Denis, Christophe Nicolas Magnan:
CN = CPCN. ICML 2006: 721-728 - 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: 129-136 - 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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