Publications
(You may also look at my Google Scholar and dblp profiles)
■ Book/Theses ■ Journal articles ■ Conference papers ■ Informal publications
2019
- A. May, A. Bagheri Garakani, Z. Lu, D. Guo, K. Liu, A. Bellet, L. Fan, M. Collins, D. Hsu, B. Kingsbury, M. Picheny and F. Sha. Kernel Approximation Methods for Speech Recognition. Journal of Machine Learning Research, to appear, 2019.
[arXiv version] [bibtex] - K. Liu and A. Bellet. Escaping the Curse of Dimensionality in Similarity Learning: Efficient Frank-Wolfe Algorithm and Generalization Bounds. Neurocomputing 333:185-199, 2019.
[paper] [arXiv version] [bibtex] - A. Bellet, R. Guerraoui and H. Hendrikx. Who started this rumor? Quantifying the natural differential privacy guarantees of gossip protocols. arXiv:1902.07138, 2019.
[paper] [bibtex] - V. Zantedeschi, A. Bellet and M. Tommasi. Communication-Efficient and Decentralized Multi-Task Boosting while Learning the Collaboration Graph. arXiv:1901.08460, 2019.
[paper] [bibtex]
2018
- W. Zheng, A. Bellet and P. Gallinari. A Distributed Frank-Wolfe Framework for Learning Low-Rank Matrices with the Trace Norm. Machine Learning 107(8-10):1457-1475, 2018.
[paper] [arXiv version] [bibtex] [code] - M. Ailem, B. Zhang, A. Bellet, P. Denis and F. Sha. A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018.
[paper] [bibtex] - R. Vogel, A. Bellet and S. Clémençon. A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization. International Conference on Machine Learning (ICML), 2018.
[paper] [supplementary] [arXiv version] [bibtex] - A. Bellet, R. Guerraoui, M. Taziki and M. Tommasi. Personalized and Private Peer-to-Peer Machine Learning. International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.
[paper] [supplementary] [arXiv version] [bibtex] - V. Zantedeschi, A. Bellet and M. Tommasi. Communication-Efficient Decentralized Boosting while Discovering the Collaboration Graph. NeurIPS Workshop on Machine Learning on the Phone and other Consumer Devices, 2018.
- A. Bellet, R. Guerraoui and H. Hendrikx. Who started this gossip? Differentially private rumor spreading. NeurIPS Workshop on Privacy Preserving Machine Learning, 2018.
- V. Zantedeschi, A. Bellet and M. Tommasi. Frank-Wolfe Boosting Décentralisé pour l'Apprentissage de Modèles Personnalisés. Conférence Francophone sur l'Apprentissage Automatique (CAp), 2018.
- A. Bellet, R. Guerraoui, M. Taziki and M. Tommasi. Personalized and Private Peer-to-Peer Machine Learning. Atelier sur la Protection de la Vie Privée (APVP), 2018.
- P. Dellenbach, A. Bellet and J. Ramon. Hiding in the Crowd: A Massively Distributed Algorithm for Private Averaging with Malicious Adversaries. arXiv:1803.09984, 2018.
[paper] [bibtex]
2017
- P. Vanhaesebrouck, A. Bellet and M. Tommasi. Decentralized Collaborative Learning of Personalized Models over Networks. International Conference on Artificial Intelligence and Statistics (AISTATS), 2017.
[paper] [supplementary] [arXiv version] [bibtex] - A. Bellet, R. Guerraoui, M. Taziki and M. Tommasi. Personalized and Private Peer-to-Peer Machine Learning. NIPS Workshop on Machine Learning on the Phone and other Consumer Devices, 2017.
[slides] - T. Le Van, A. Bellet and J. Ramon. Decentralized and Privacy-Aware Learning of Traversal Time Models. ECML/PKDD workshop on Data Mining with Secure Computation, 2017.
- P. Vanhaesebrouck, A. Bellet and M. Tommasi. Apprentissage collaboratif et décentralisé de modèles personnels sur un réseau. Conférence Francophone sur l'Apprentissage Automatique (CAp), 2017.
- P. Dellenbach, J. Ramon and A. Bellet. Un protocole décentralisé et robuste pour le calcul de moyenne sous contrainte de vie privée. Conférence Francophone sur l'Apprentissage Automatique (CAp), 2017.
2016
- S. Clémençon, I. Colin and A. Bellet. Scaling-up Empirical Risk Minimization: Optimization of Incomplete U-statistics. Journal of Machine Learning Research 17(76):1-36, 2016.
[paper] [arXiv version] [bibtex] - A. Bellet, J.F. Bernabeu, A. Habrard and M. Sebban. Learning Discriminative Tree Edit Similarities for Linear Classification — Application to Melody Recognition. Neurocomputing 214:155-161, 2016.
[paper] [bibtex] - G. Papa, A. Bellet and S. Clémençon. On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability. Annual Conference on Neural Information Processing Systems (NIPS), 2016.
[paper] [supplementary] [bibtex] - I. Colin, A. Bellet, J. Salmon and S. Clémençon. Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions. International Conference on Machine Learning (ICML), 2016.
[paper] [supplementary] [arXiv version] [bibtex] - Z. Lu, D. Guo, A. Bagheri Garakani, K. Liu, A. May, A. Bellet, L. Fan, M. Collins, B. Kingsbury, M. Picheny and F. Sha. A Comparison Between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016.
[paper] [arXiv version] [bibtex] - P. Dellenbach, J. Ramon and A. Bellet. A Decentralized and Robust Protocol for Private Averaging over Highly Distributed Data. NIPS workshop on Private Multi-Party Machine Learning, 2016.
- I. Colin, A. Bellet, J. Salmon and S. Clémençon. Un algorithme de Gossip pour l’optimisation décentralisée de fonctions sur les paires. Conférence Francophone sur l'Apprentissage Automatique (CAp), 2016.
2015
- A. Bellet, A. Habrard and M. Sebban. Metric Learning. Morgan & Claypool Publishers, 2015.
[publisher's website] [bibtex] - A. Bellet and A. Habrard. Robustness and Generalization for Metric Learning. Neurocomputing 151(1):259–267, 2015.
[paper] [arXiv version] [bibtex] - I. Colin, A. Bellet, J. Salmon and S. Clémençon. Extending Gossip Algorithms to Distributed Estimation of U-statistics. Annual Conference on Neural Information Processing Systems (NIPS), 2015.
[paper] [supplementary] [arXiv version] [bibtex] [code] - G. Papa, S. Clémençon and A. Bellet. SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk. Annual Conference on Neural Information Processing Systems (NIPS), 2015.
[paper] [supplementary] [bibtex] - S. Clémençon, A. Bellet, O. Jelassi and G. Papa. Scalability of Stochastic Gradient Descent based on "Smart" Sampling Techniques. INNS Conference on Big Data (INNS-BigData), 2015.
[bibtex] - K. Liu, A. Bellet and F. Sha. Similarity Learning for High-Dimensional Sparse Data. International Conference on Artificial Intelligence and Statistics (AISTATS), 2015.
[paper] [arXiv version] [bibtex] - A. Bellet, Y. Liang, A. Bagheri Garakani, M.-F. Balcan and F. Sha. A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning. SIAM International Conference on Data Mining (SDM), 2015.
[paper] [extended version] [bibtex] [code]
2014
- A. Bellet, A. Habrard, E. Morvant and M. Sebban. Learning A Priori Constrained Weighted Majority Votes. Machine Learning 97(1-2):129-154, 2014.
[paper] [bibtex] - Y. Shi, A. Bellet and F. Sha. Sparse Compositional Metric Learning. AAAI Conference on Artificial Intelligence (AAAI), 2014.
[paper] [extended version] [bibtex] [code] - A. Bellet, Y. Liang, A. Bagheri Garakani, M.-F. Balcan and F. Sha. Distributed Frank-Wolfe Algorithm: A Unified Framework for Communication-Efficient Sparse Learning. ICML workshop on New Learning Frameworks and Models for Big Data, 2014.
[slides] - Z. Lu, A. May, K. Liu, A. Bagheri Garakani, D. Guo, A. Bellet, L. Fan, M. Collins, B. Kingsbury, M. Picheny and F. Sha. How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets. arXiv:1411.4000, 2014.
[paper] [bibtex]
2013
- A. Bellet, A. Habrard, E. Morvant and M. Sebban. Vote de majorité a priori contraint pour la classification binaire : spécification au cas des plus proches voisins. Conférence Francophone sur l'Apprentissage Automatique (CAp), 2013.
- A. Bellet, A. Habrard and M. Sebban. A Survey on Metric Learning for Feature Vectors and Structured Data. arXiv:1306.6709, 2013.
[paper] [bibtex]
2012
- A. Bellet. Supervised Metric Learning with Generalization Guarantees. Ph.D. thesis, Université Jean Monnet de Saint-Étienne, 2012.
[manuscript] [slides] - A. Bellet, A. Habrard and M. Sebban. Good edit similarity learning by loss minimization. Machine Learning 89(1):5-35, 2012.
[paper] [bibtex] [code] - A. Bellet, A. Habrard and M. Sebban. Similarity Learning for Provably Accurate Sparse Linear Classification. International Conference on Machine Learning (ICML), 2012.
[paper] [slides] [video] [bibtex] - A. Bellet, A. Habrard and M. Sebban. Apprentissage de bonnes similarités pour la classification linéaire parcimonieuse. Conférence Francophone sur l'Apprentissage Automatique (CAp), 2012.
2011
- A. Bellet, A. Habrard and M. Sebban. Learning Good Edit Similarities with Generalization Guarantees. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2011.
[paper] [slides] [video] [bibtex] [code] - A. Bellet, A. Habrard and M. Sebban. An Experimental Study on Learning with Good Edit Similarity Functions. International Conference on Tools with Artificial Intelligence (ICTAI), 2011.
[paper] [bibtex] - A. Bellet, A. Habrard and M. Sebban. Good Similarity Learning for Structured Data. NIPS workshop "Beyond Mahalanobis: Supervised Large-Scale Learning of Similarity", 2011.
- A. Bellet, A. Habrard and M. Sebban. Apprentissage Parcimonieux à partir de Fonctions de Similarité d'Edition (ε,γ,τ)-Good. Conférence Francophone sur l'Apprentissage Automatique (CAp), 2011.
2010
- A. Bellet, M. Bernard, T. Murgue and M. Sebban. Learning State Machine-based String Edit Kernels. Pattern Recognition 43(6):2330-2339, 2010.
[paper] [bibtex]
2009
- A. Bellet, M. Bernard, T. Murgue and M. Sebban. Apprentissage de noyaux d'édition de séquences. Conférence Francophone sur l'Apprentissage Automatique (CAp), 2009.