Learning state machine-based string edit kernels. - Aurélien Bellet; Marc Bernard; Thierry Murgue; Marc Sebban (Pattern Recognit., 2010)

An Experimental Study on Learning with Good Edit Similarity Functions. - Aurélien Bellet; Marc Sebban; Amaury Habrard (ICTAI, 2011)

Learning Good Edit Similarities with Generalization Guarantees. - Aurélien Bellet; Amaury Habrard; Marc Sebban (ECML/PKDD (1), 2011)

Similarity Learning for Provably Accurate Sparse Linear Classification. - Aurélien Bellet; Amaury Habrard; Marc Sebban (ICML, 2012)

Good edit similarity learning by loss minimization. - Aurélien Bellet; Amaury Habrard; Marc Sebban (Mach. Learn., 2012)

Supervised metric learning with generalization guarantees. (Apprentissage supervisé de métriques avec garanties en généralisation). - Aurélien Bellet (2012)

Supervised Metric Learning with Generalization Guarantees. - Aurélien Bellet (CoRR, 2013)

A Survey on Metric Learning for Feature Vectors and Structured Data. - Aurélien Bellet; Amaury Habrard; Marc Sebban (CoRR, 2013)

Sparse Compositional Metric Learning. - Yuan Shi; Aurélien Bellet; Fei Sha (AAAI, 2014)

Distributed Frank-Wolfe Algorithm: A Unified Framework for Communication-Efficient Sparse Learning. - Aurélien Bellet; Yingyu Liang; Alireza Bagheri Garakani; Maria-Florina Balcan; Fei Sha (CoRR, 2014)

How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets. - Zhiyun Lu; Avner May; Kuan Liu; Alireza Bagheri Garakani; Dong Guo; Aurélien Bellet; Linxi Fan; Michael Collins 001; Brian Kingsbury; Michael Picheny; Fei Sha (CoRR, 2014)

Learning a priori constrained weighted majority votes. - Aurélien Bellet; Amaury Habrard; Emilie Morvant; Marc Sebban (Mach. Learn., 2014)

Similarity Learning for High-Dimensional Sparse Data. - Kuan Liu; Aurélien Bellet; Fei Sha (AISTATS, 2015)

Scalability of Stochastic Gradient Descent based on "Smart" Sampling Techniques. - Stéphan Clémençon; Aurélien Bellet; Ons Jelassi; Guillaume Papa (INNS Conference on Big Data, 2015)

Extending Gossip Algorithms to Distributed Estimation of U-statistics. - Igor Colin; Aurélien Bellet; Joseph Salmon; Stéphan Clémençon (NIPS, 2015)

SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk. - Guillaume Papa; Stéphan Clémençon; Aurélien Bellet (NIPS, 2015)

A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning. - Aurélien Bellet; Yingyu Liang; Alireza Bagheri Garakani; Maria-Florina Balcan; Fei Sha (SDM, 2015)

Robustness and generalization for metric learning. - Aurélien Bellet; Amaury Habrard (Neurocomputing, 2015)

Metric Learning - Aurélien Bellet; Amaury Habrard; Marc Sebban (Morgan & Claypool Publishers, 2015)

A comparison between deep neural nets and kernel acoustic models for speech recognition. - Zhiyun Lu; Dong Guo; Alireza Bagheri Garakani; Kuan Liu; Avner May; Aurélien Bellet; Linxi Fan; Michael Collins 001; Brian Kingsbury; Michael Picheny; Fei Sha (ICASSP, 2016)

Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions. - Igor Colin; Aurélien Bellet; Joseph Salmon; Stéphan Clémençon (ICML, 2016)

On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability. - Guillaume Papa; Aurélien Bellet; Stéphan Clémençon (NIPS, 2016)

Learning discriminative tree edit similarities for linear classification - Application to melody recognition. - Aurélien Bellet; José Francisco Bernabeu; Amaury Habrard; Marc Sebban (Neurocomputing, 2016)

Scaling-up Empirical Risk Minimization: Optimization of Incomplete $U$-statistics. - Stéphan Clémençon; Igor Colin; Aurélien Bellet (J. Mach. Learn. Res., 2016)

Decentralized Collaborative Learning of Personalized Models over Networks. - Paul Vanhaesebrouck; Aurélien Bellet; Marc Tommasi (AISTATS, 2017)

Fast and Differentially Private Algorithms for Decentralized Collaborative Machine Learning. - Aurélien Bellet; Rachid Guerraoui; Mahsa Taziki; Marc Tommasi (CoRR, 2017)

Personalized and Private Peer-to-Peer Machine Learning. - Aurélien Bellet; Rachid Guerraoui; Mahsa Taziki; Marc Tommasi (AISTATS, 2018)

A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images. - Melissa Ailem; Bowen Zhang 002; Aurélien Bellet; Pascal Denis; Fei Sha (EMNLP, 2018)

A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization. - Robin Vogel; Aurélien Bellet; Stéphan Clémençon (ICML, 2018)

Hiding in the Crowd: A Massively Distributed Algorithm for Private Averaging with Malicious Adversaries. - Pierre Dellenbach; Aurélien Bellet; Jan Ramon (CoRR, 2018)

A distributed Frank-Wolfe framework for learning low-rank matrices with the trace norm. - Wenjie Zheng 001; Aurélien Bellet; Patrick Gallinari (Mach. Learn., 2018)

Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion? - Brij Mohan Lal Srivastava; Aurélien Bellet; Marc Tommasi; Emmanuel Vincent 001 (INTERSPEECH, 2019)

Trade-Offs in Large-Scale Distributed Tuplewise Estimation And Learning. - Robin Vogel; Aurélien Bellet; Stéphan Clémençon; Ons Jelassi; Guillaume Papa (ECML/PKDD (2), 2019)

Communication-Efficient and Decentralized Multi-Task Boosting while Learning the Collaboration Graph. - Valentina Zantedeschi; Aurélien Bellet; Marc Tommasi (CoRR, 2019)

Who started this rumor? Quantifying the natural differential privacy guarantees of gossip protocols. - Aurélien Bellet; Rachid Guerraoui; Hadrien Hendrikx (CoRR, 2019)

Advances and Open Problems in Federated Learning. - Peter Kairouz; H. Brendan McMahan; Brendan Avent; Aurélien Bellet; Mehdi Bennis; Arjun Nitin Bhagoji; Kallista A. Bonawitz; Zachary Charles; Graham Cormode; Rachel Cummings; Rafael G. L. D'Oliveira; Salim El Rouayheb; et al. (CoRR, 2019)

Escaping the curse of dimensionality in similarity learning: Efficient Frank-Wolfe algorithm and generalization bounds. - Kuan Liu; Aurélien Bellet (Neurocomputing, 2019)

Kernel Approximation Methods for Speech Recognition. - Avner May; Alireza Bagheri Garakani; Zhiyun Lu; Dong Guo; Kuan Liu; Aurélien Bellet; Linxi Fan; Michael Collins 001; Daniel Hsu 001; Brian Kingsbury; Michael Picheny; Fei Sha (J. Mach. Learn. Res., 2019)

Private Protocols for U-Statistics in the Local Model and Beyond. - James Bell 001; Aurélien Bellet; Adrià Gascón; Tejas Kulkarni (AISTATS, 2020)

Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs. - Valentina Zantedeschi; Aurélien Bellet; Marc Tommasi (AISTATS, 2020)

Evaluating Voice Conversion-Based Privacy Protection against Informed Attackers. - Brij Mohan Lal Srivastava; Nathalie Vauquier; Md. Sahidullah; Aurélien Bellet; Marc Tommasi; Emmanuel Vincent 001 (ICASSP, 2020)

A Comparative Study of Speech Anonymization Metrics. - Mohamed Maouche; Brij Mohan Lal Srivastava; Nathalie Vauquier; Aurélien Bellet; Marc Tommasi; Emmanuel Vincent 001 (INTERSPEECH, 2020)

Design Choices for X-Vector Based Speaker Anonymization. - Brij Mohan Lal Srivastava; Natalia A. Tomashenko; Xin Wang 037; Emmanuel Vincent 001; Junichi Yamagishi; Mohamed Maouche; Aurélien Bellet; Marc Tommasi (INTERSPEECH, 2020)

Who Started This Rumor? Quantifying the Natural Differential Privacy of Gossip Protocols. - Aurélien Bellet; Rachid Guerraoui; Hadrien Hendrikx (DISC, 2020)

Learning Fair Scoring Functions: Fairness Definitions, Algorithms and Generalization Bounds for Bipartite Ranking. - Robin Vogel; Aurélien Bellet; Stéphan Clémençon (CoRR, 2020)

Distributed Differentially Private Averaging with Improved Utility and Robustness to Malicious Parties. - César Sabater; Aurélien Bellet; Jan Ramon (CoRR, 2020)

metric-learn: Metric Learning Algorithms in Python. - William de Vazelhes; CJ Carey; Yuan Tang; Nathalie Vauquier; Aurélien Bellet (J. Mach. Learn. Res., 2020)

Contributions to Decentralized and Privacy-Preserving Machine Learning. - Aurélien Bellet (2021)

Learning Fair Scoring Functions: Bipartite Ranking under ROC-based Fairness Constraints. - Robin Vogel; Aurélien Bellet; Stéphan Clémençon (AISTATS, 2021)

Federated Multi-Task Learning under a Mixture of Distributions. - Othmane Marfoq; Giovanni Neglia; Aurélien Bellet; Laetitia Kameni; Richard Vidal (NeurIPS, 2021)

D-Cliques: Compensating NonIIDness in Decentralized Federated Learning with Topology. - Aurélien Bellet; Anne-Marie Kermarrec; Erick Lavoie (CoRR, 2021)

Mitigating Leakage from Data Dependent Communications in Decentralized Computing using Differential Privacy. - Riad Ladjel; Nicolas Anciaux; Aurélien Bellet; Guillaume Scerri (CoRR, 2021)

Advances and Open Problems in Federated Learning. - Peter Kairouz; H. Brendan McMahan; Brendan Avent; Aurélien Bellet; Mehdi Bennis; Arjun Nitin Bhagoji; Kallista A. Bonawitz; Zachary Charles; Graham Cormode; Rachel Cummings; Rafael G. L. D'Oliveira; Hubert Eichner; et al. (Found. Trends Mach. Learn., 2021)

Reconstructing Genotypes in Private Genomic Databases from Genetic Risk Scores. - Brooks Paige; James Bell 001; Aurélien Bellet; Adrià Gascón; Daphne Ezer (J. Comput. Biol., 2021)

Privacy Amplification by Decentralization. - Edwige Cyffers; Aurélien Bellet (AISTATS, 2022)

Differentially Private Federated Learning on Heterogeneous Data. - Maxence Noble; Aurélien Bellet; Aymeric Dieuleveut (AISTATS, 2022)

Fair NLP Models with Differentially Private Text Encoders. - Gaurav Maheshwari 004; Pascal Denis; Mikaela Keller; Aurélien Bellet (EMNLP (Findings), 2022)

Differentially Private Coordinate Descent for Composite Empirical Risk Minimization. - Paul Mangold; Aurélien Bellet; Joseph Salmon; Marc Tommasi (ICML, 2022)

Enhancing Speech Privacy with Slicing. - Mohamed Maouche; Brij Mohan Lal Srivastava; Nathalie Vauquier; Aurélien Bellet; Marc Tommasi; Emmanuel Vincent 001 (INTERSPEECH, 2022)

Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging. - Edwige Cyffers; Mathieu Even; Aurélien Bellet; Laurent Massoulié (NeurIPS, 2022)

FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. - Jean Ogier du Terrail; Samy-Safwan Ayed; Edwige Cyffers; Felix Grimberg; Chaoyang He 001; Regis Loeb; Paul Mangold; Tanguy Marchand; Othmane Marfoq; Erum Mushtaq; Boris Muzellec; Constantin Philippenko; et al. (NeurIPS, 2022)

D-Cliques: Compensating for Data Heterogeneity with Topology in Decentralized Federated Learning. - Aurélien Bellet; Anne-Marie Kermarrec; Erick Lavoie (SRDS, 2022)

Yes, Topology Matters in Decentralized Optimization: Refined Convergence and Topology Learning under Heterogeneous Data. - Batiste Le Bars; Aurélien Bellet; Marc Tommasi; Anne-Marie Kermarrec (CoRR, 2022)

Fairness Certificates for Differentially Private Classification. - Paul Mangold; Michaël Perrot; Aurélien Bellet; Marc Tommasi (CoRR, 2022)

PEPPER: Empowering User-Centric Recommender Systems over Gossip Learning. - Yacine Belal; Aurélien Bellet; Sonia Ben Mokhtar; Vlad Nitu (Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2022)

An accurate, scalable and verifiable protocol for federated differentially private averaging. - César Sabater; Aurélien Bellet; Jan Ramon (Mach. Learn., 2022)

Privacy and Utility of X-Vector Based Speaker Anonymization. - Brij Mohan Lal Srivastava; Mohamed Maouche; Md. Sahidullah; Emmanuel Vincent 001; Aurélien Bellet; Marc Tommasi; Natalia A. Tomashenko; Xin Wang 037; Junichi Yamagishi (IEEE ACM Trans. Audio Speech Lang. Process., 2022)

Collaborative Algorithms for Online Personalized Mean Estimation. - Mahsa Asadi; Aurélien Bellet; Odalric-Ambrym Maillard; Marc Tommasi (Trans. Mach. Learn. Res., 2022)

Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Data. - Batiste Le Bars; Aurélien Bellet; Marc Tommasi; Erick Lavoie; Anne-Marie Kermarrec (AISTATS, 2023)

High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent. - Paul Mangold; Aurélien Bellet; Joseph Salmon; Marc Tommasi (AISTATS, 2023)

Fair Without Leveling Down: A New Intersectional Fairness Definition. - Gaurav Maheshwari 004; Aurélien Bellet; Pascal Denis; Mikaela Keller (EMNLP, 2023)

From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning. - Edwige Cyffers; Aurélien Bellet; Debabrota Basu (ICML, 2023)

One-Shot Federated Conformal Prediction. - Pierre Humbert; Batiste Le Bars; Aurélien Bellet; Sylvain Arlot (ICML, 2023)

Differential Privacy has Bounded Impact on Fairness in Classification. - Paul Mangold; Michaël Perrot; Aurélien Bellet; Marc Tommasi (ICML, 2023)

GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation. - Sina Sajadmanesh; Ali Shahin Shamsabadi; Aurélien Bellet; Daniel Gatica-Perez (USENIX Security Symposium, 2023)

How to Capture Intersectional Fairness. - Gaurav Maheshwari 004; Aurélien Bellet; Pascal Denis; Mikaela Keller (CoRR, 2023)

Improved Stability and Generalization Analysis of the Decentralized SGD Algorithm. - Batiste Le Bars; Aurélien Bellet; Marc Tommasi (CoRR, 2023)

Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration. - Clément Pierquin; Aurélien Bellet; Marc Tommasi; Matthieu Boussard (CoRR, 2023)

Differentially Private Speaker Anonymization. - Ali Shahin Shamsabadi; Brij Mohan Lal Srivastava; Aurélien Bellet; Nathalie Vauquier; Emmanuel Vincent 001; Mohamed Maouche; Marc Tommasi; Nicolas Papernot (Proc. Priv. Enhancing Technol., 2023)

The Relative Gaussian Mechanism and its Application to Private Gradient Descent. - Hadrien Hendrikx; Paul Mangold; Aurélien Bellet (AISTATS, 2024)

DP-SGD Without Clipping: The Lipschitz Neural Network Way. - Louis Béthune; Thomas Massena; Thibaut Boissin; Aurélien Bellet; Franck Mamalet; Yannick Prudent; Corentin Friedrich; Mathieu Serrurier; David Vigouroux (ICLR, 2024)

Confidential-DPproof: Confidential Proof of Differentially Private Training. - Ali Shahin Shamsabadi; Gefei Tan; Tudor Cebere; Aurélien Bellet; Hamed Haddadi; Nicolas Papernot; Xiao Wang 012; Adrian Weller (ICLR, 2024)

Improved Stability and Generalization Guarantees of the Decentralized SGD Algorithm. - Batiste Le Bars; Aurélien Bellet; Marc Tommasi; Kevin Scaman; Giovanni Neglia (ICML, 2024)

Differentially Private Decentralized Learning with Random Walks. - Edwige Cyffers; Aurélien Bellet; Jalaj Upadhyay (ICML, 2024)

Privacy Attacks in Decentralized Learning. - Abdellah El Mrini; Edwige Cyffers; Aurélien Bellet (ICML, 2024)

Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration via Shift Reduction Lemmas. - Clément Pierquin; Aurélien Bellet; Marc Tommasi; Matthieu Boussard (ICML, 2024)

Synthetic Data Generation for Intersectional Fairness by Leveraging Hierarchical Group Structure. - Gaurav Maheshwari 004; Aurélien Bellet; Pascal Denis; Mikaela Keller (CoRR, 2024)

Nebula: Efficient, Private and Accurate Histogram Estimation. - Ali Shahin Shamsabadi; Peter Snyder; Ralph Giles; Aurélien Bellet; Hamed Haddadi (CoRR, 2024)

Federated Causal Inference: Multi-Centric ATE Estimation beyond Meta-Analysis. - Rémi Khellaf; Aurélien Bellet; Julie Josse (CoRR, 2024)

Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis. - Rémi Khellaf; Aurélien Bellet; Julie Josse (AISTATS, 2025)

Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model. - Tudor Ioan Cebere; Aurélien Bellet; Nicolas Papernot (ICLR, 2025)

Optimal Transport with Heterogeneously Missing Data. - Linus Bleistein; Aurélien Bellet; Julie Josse (CoRR, 2025)

Federated Causal Inference from Multi-Site Observational Data via Propensity Score Aggregation. - Rémi Khellaf; Aurélien Bellet; Julie Josse (CoRR, 2025)

Model Agnostic Differentially Private Causal Inference. - Christian Janos Lebeda; Mathieu Even; Aurélien Bellet; Julie Josse (CoRR, 2025)

Private Rate-Constrained Optimization with Applications to Fair Learning. - Mohammad Yaghini; Tudor Cebere; Michael Menart; Aurélien Bellet; Nicolas Papernot (CoRR, 2025)

Privacy Amplification Through Synthetic Data: Insights from Linear Regression. - Clément Pierquin; Aurélien Bellet; Marc Tommasi; Matthieu Boussard (CoRR, 2025)

Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction. - Vincent Roca; Marc Tommasi; Paul Andrey; Aurélien Bellet; Markus D. Schirmer; Hilde Henon; Laurent Puy; Julien Ramon; Grégory Kuchcinski; Martin Bretzner; Renaud Lopes (CoRR, 2025)

Generalization under Byzantine & Poisoning Attacks: Tight Stability Bounds in Robust Distributed Learning. - Thomas Boudou; Batiste Le Bars; Nirupam Gupta; Aurélien Bellet (CoRR, 2025)