Aurélien Bellet

Julie Josse

CoRR, 2024

Nebula: Efficient, Private and Accurate Histogram Estimation.

[BibT_eX]

[DOI]

CoRR, 2024

Synthetic Data Generation for Intersectional Fairness by Leveraging Hierarchical Group Structure.

[BibT_eX]

[DOI]

CoRR, 2024

Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model.

[BibT_eX]

[DOI]

Tudor Cebere

Nicolas Papernot

CoRR, 2024

Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration via Shift Reduction Lemmas.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Privacy Attacks in Decentralized Learning.

[BibT_eX]

[DOI]

Abdellah El Mrini

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Differentially Private Decentralized Learning with Random Walks.

[BibT_eX]

[DOI]

Jalaj Upadhyay

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Improved Stability and Generalization Guarantees of the Decentralized SGD Algorithm.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Confidential-DPproof: Confidential Proof of Differentially Private Training.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

DP-SGD Without Clipping: The Lipschitz Neural Network Way.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

The Relative Gaussian Mechanism and its Application to Private Gradient Descent.

[BibT_eX]

[DOI]

Hadrien Hendrikx

Paul Mangold

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Differentially Private Speaker Anonymization.

[BibT_eX]

[DOI]

Proc. Priv. Enhancing Technol., January, 2023

Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration.

[BibT_eX]

[DOI]

CoRR, 2023

Improved Stability and Generalization Analysis of the Decentralized SGD Algorithm.

[BibT_eX]

[DOI]

Batiste Le Bars

CoRR, 2023

How to Capture Intersectional Fairness.

[BibT_eX]

[DOI]

CoRR, 2023

GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation.

[BibT_eX]

[DOI]

Sina Sajadmanesh

Daniel Gatica-Perez

Proceedings of the 32nd USENIX Security Symposium, 2023

Differential Privacy has Bounded Impact on Fairness in Classification.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

One-Shot Federated Conformal Prediction.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning.

[BibT_eX]

[DOI]

Debabrota Basu

Proceedings of the International Conference on Machine Learning, 2023

Fair Without Leveling Down: A New Intersectional Fairness Definition.

[BibT_eX]

[DOI]

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Data.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Collaborative Algorithms for Online Personalized Mean Estimation.

[BibT_eX]

[DOI]

Mahsa Asadi

Odalric-Ambrym Maillard

Trans. Mach. Learn. Res., 2022

Privacy and Utility of X-Vector Based Speaker Anonymization.

[BibT_eX]

[DOI]

Natalia A. Tomashenko

Xin Wang

Junichi Yamagishi

IEEE ACM Trans. Audio Speech Lang. Process., 2022

An accurate, scalable and verifiable protocol for federated differentially private averaging.

[BibT_eX]

[DOI]

César Sabater

Jan Ramon

Mach. Learn., 2022

PEPPER: Empowering User-Centric Recommender Systems over Gossip Learning.

[BibT_eX]

[DOI]

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2022

Fairness Certificates for Differentially Private Classification.

[BibT_eX]

[DOI]

CoRR, 2022

Yes, Topology Matters in Decentralized Optimization: Refined Convergence and Topology Learning under Heterogeneous Data.

[BibT_eX]

[DOI]

CoRR, 2022

D-Cliques: Compensating for Data Heterogeneity with Topology in Decentralized Federated Learning.

[BibT_eX]

[DOI]

Anne-Marie Kermarrec

Erick Lavoie

Proceedings of the 41st International Symposium on Reliable Distributed Systems, 2022

FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Enhancing Speech Privacy with Slicing.

[BibT_eX]

[DOI]

Mohamed Maouche

Proceedings of the 23rd Annual Conference of the International Speech Communication Association, 2022

Differentially Private Coordinate Descent for Composite Empirical Risk Minimization.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Fair NLP Models with Differentially Private Text Encoders.

[BibT_eX]

[DOI]

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

Differentially Private Federated Learning on Heterogeneous Data.

[BibT_eX]

[DOI]

Maxence Noble

Aymeric Dieuleveut

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Privacy Amplification by Decentralization.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Reconstructing Genotypes in Private Genomic Databases from Genetic Risk Scores.

[BibT_eX]

[DOI]

J. Comput. Biol., May, 2021

Advances and Open Problems in Federated Learning.

[BibT_eX]

[DOI]

Rafael G. L. D'Oliveira

Ananda Theertha Suresh

Found. Trends Mach. Learn., 2021

Mitigating Leakage from Data Dependent Communications in Decentralized Computing using Differential Privacy.

[BibT_eX]

[DOI]

CoRR, 2021

D-Cliques: Compensating NonIIDness in Decentralized Federated Learning with Topology.

[BibT_eX]

[DOI]

Anne-Marie Kermarrec

Erick Lavoie

CoRR, 2021

Federated Multi-Task Learning under a Mixture of Distributions.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning Fair Scoring Functions: Bipartite Ranking under ROC-based Fairness Constraints.

[BibT_eX]

[DOI]

Robin Vogel

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Contributions to Decentralized and Privacy-Preserving Machine Learning.

[BibT_eX]

[DOI]

, 2021

2020

metric-learn: Metric Learning Algorithms in Python.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2020

Distributed Differentially Private Averaging with Improved Utility and Robustness to Malicious Parties.

[BibT_eX]

[DOI]

César Sabater

Jan Ramon

CoRR, 2020

Learning Fair Scoring Functions: Fairness Definitions, Algorithms and Generalization Bounds for Bipartite Ranking.

[BibT_eX]

[DOI]

Robin Vogel

CoRR, 2020

Who Started This Rumor? Quantifying the Natural Differential Privacy of Gossip Protocols.

[BibT_eX]

[DOI]

Rachid Guerraoui

Hadrien Hendrikx

Proceedings of the 34th International Symposium on Distributed Computing, 2020

Design Choices for X-Vector Based Speaker Anonymization.

[BibT_eX]

[DOI]

Natalia A. Tomashenko

Proceedings of the 21st Annual Conference of the International Speech Communication Association, 2020

A Comparative Study of Speech Anonymization Metrics.

[BibT_eX]

[DOI]

Mohamed Maouche

Proceedings of the 21st Annual Conference of the International Speech Communication Association, 2020

Evaluating Voice Conversion-Based Privacy Protection against Informed Attackers.

[BibT_eX]

[DOI]

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs.

[BibT_eX]

[DOI]

Valentina Zantedeschi

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Private Protocols for U-Statistics in the Local Model and Beyond.

[BibT_eX]

[DOI]

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Kernel Approximation Methods for Speech Recognition.

[BibT_eX]

[DOI]

Avner May

J. Mach. Learn. Res., 2019

Escaping the curse of dimensionality in similarity learning: Efficient Frank-Wolfe algorithm and generalization bounds.

[BibT_eX]

[DOI]

Kuan Liu

Neurocomputing, 2019

Advances and Open Problems in Federated Learning.

[BibT_eX]

[DOI]

Rafael G. L. D'Oliveira

Ananda Theertha Suresh

CoRR, 2019

Who started this rumor? Quantifying the natural differential privacy guarantees of gossip protocols.

[BibT_eX]

[DOI]

Rachid Guerraoui

Hadrien Hendrikx

CoRR, 2019

Communication-Efficient and Decentralized Multi-Task Boosting while Learning the Collaboration Graph.

[BibT_eX]

[DOI]

Valentina Zantedeschi

CoRR, 2019

Trade-Offs in Large-Scale Distributed Tuplewise Estimation And Learning.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion?

[BibT_eX]

[DOI]

Emmanuel Vincent

Proceedings of the 20th Annual Conference of the International Speech Communication Association, 2019

2018

A distributed Frank-Wolfe framework for learning low-rank matrices with the trace norm.

[BibT_eX]

[DOI]

Wenjie Zheng

Patrick Gallinari

Mach. Learn., 2018

Hiding in the Crowd: A Massively Distributed Algorithm for Private Averaging with Malicious Adversaries.

[BibT_eX]

[DOI]

Pierre Dellenbach

Jan Ramon

CoRR, 2018

A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization.

[BibT_eX]

[DOI]

Robin Vogel

Proceedings of the 35th International Conference on Machine Learning, 2018

A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images.

[BibT_eX]

[DOI]

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31, 2018

Personalized and Private Peer-to-Peer Machine Learning.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Fast and Differentially Private Algorithms for Decentralized Collaborative Machine Learning.

[BibT_eX]

[DOI]

CoRR, 2017

Decentralized Collaborative Learning of Personalized Models over Networks.

[BibT_eX]

[DOI]

Paul Vanhaesebrouck

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Scaling-up Empirical Risk Minimization: Optimization of Incomplete $U$-statistics.

[BibT_eX]

[DOI]

Igor Colin

J. Mach. Learn. Res., 2016

Learning discriminative tree edit similarities for linear classification - Application to melody recognition.

[BibT_eX]

[DOI]

José Francisco Bernabeu

Neurocomputing, 2016

On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability.

[BibT_eX]

[DOI]

Guillaume Papa

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions.

[BibT_eX]

[DOI]

Proceedings of the 33nd International Conference on Machine Learning, 2016

A comparison between deep neural nets and kernel acoustic models for speech recognition.

[BibT_eX]

[DOI]

Zhiyun Lu

Dong Guo

Proceedings of the 2016 IEEE International Conference on Acoustics, 2016

2015

Metric Learning

[BibT_eX]

[DOI]

Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers, ISBN: 978-3-031-01572-4, 2015

Robustness and generalization for metric learning.

[BibT_eX]

[DOI]

Neurocomputing, 2015

A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning.

[BibT_eX]

[DOI]

Yingyu Liang

Maria-Florina Balcan

Proceedings of the 2015 SIAM International Conference on Data Mining, Vancouver, BC, Canada, April 30, 2015

SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk.

[BibT_eX]

[DOI]

Guillaume Papa

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Extending Gossip Algorithms to Distributed Estimation of U-statistics.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Scalability of Stochastic Gradient Descent based on "Smart" Sampling Techniques.

[BibT_eX]

[DOI]

Proceedings of the INNS Conference on Big Data 2015, 2015

Similarity Learning for High-Dimensional Sparse Data.

[BibT_eX]

[DOI]

Kuan Liu

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Learning a priori constrained weighted majority votes.

[BibT_eX]

[DOI]

Mach. Learn., 2014

How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets.

[BibT_eX]

[DOI]

Zhiyun Lu

Avner May

Kuan Liu

CoRR, 2014

Distributed Frank-Wolfe Algorithm: A Unified Framework for Communication-Efficient Sparse Learning.

[BibT_eX]

[DOI]

Yingyu Liang

Maria-Florina Balcan

CoRR, 2014

Sparse Compositional Metric Learning.

[BibT_eX]

[DOI]

Yuan Shi

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2013

A Survey on Metric Learning for Feature Vectors and Structured Data.

[BibT_eX]

[DOI]

CoRR, 2013

Supervised Metric Learning with Generalization Guarantees.

[BibT_eX]

[DOI]

CoRR, 2013

2012

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

[BibT_eX]

[DOI]

PhD thesis, 2012

Good edit similarity learning by loss minimization.

[BibT_eX]

[DOI]

Mach. Learn., 2012

Similarity Learning for Provably Accurate Sparse Linear Classification.

[BibT_eX]

[DOI]

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Learning Good Edit Similarities with Generalization Guarantees.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2011

An Experimental Study on Learning with Good Edit Similarity Functions.

[BibT_eX]

[DOI]