Aurélien Bellet

research

∙ 08/29/2023

The Relative Gaussian Mechanism and its Application to Private Gradient Descent

The Gaussian Mechanism (GM), which consists in adding Gaussian noise to ...

0 Hadrien Hendrikx, et al. ∙

research

∙ 06/05/2023

Improved Stability and Generalization Analysis of the Decentralized SGD Algorithm

This paper presents a new generalization error analysis for the Decentra...

0 Batiste Le Bars, et al. ∙

research

∙ 05/25/2023

DP-SGD Without Clipping: The Lipschitz Neural Network Way

State-of-the-art approaches for training Differentially Private (DP) Dee...

0 Louis Béthune, et al. ∙

research

∙ 05/21/2023

How to Capture Intersectional Fairness

In this work, we tackle the problem of intersectional group fairness in ...

0 Gaurav Maheshwari, et al. ∙

research

∙ 02/24/2023

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

We study differentially private (DP) machine learning algorithms as inst...

3 Edwige Cyffers, et al. ∙

research

∙ 02/13/2023

One-Shot Federated Conformal Prediction

In this paper, we introduce a conformal prediction method to construct p...

0 Pierre Humbert, et al. ∙

research

∙ 10/28/2022

Fairness Certificates for Differentially Private Classification

In this work, we theoretically study the impact of differential privacy ...

0 Paul Mangold, et al. ∙

research

∙ 08/24/2022

Collaborative Algorithms for Online Personalized Mean Estimation

We consider an online estimation problem involving a set of agents. Each...

0 Mahsa Asadi, et al. ∙

research

∙ 08/09/2022

PEPPER: Empowering User-Centric Recommender Systems over Gossip Learning

Recommender systems are proving to be an invaluable tool for extracting ...

0 Yacine Belal, et al. ∙

research

∙ 07/04/2022

High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent

In this paper, we study differentially private empirical risk minimizati...

0 Paul Mangold, et al. ∙

research

∙ 06/10/2022

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

Decentralized optimization is increasingly popular in machine learning f...

0 Edwige Cyffers, et al. ∙

research

∙ 05/12/2022

Fair NLP Models with Differentially Private Text Encoders

Encoded text representations often capture sensitive attributes about in...

0 Gaurav Maheshwari, et al. ∙

research

∙ 03/02/2022

GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation

Graph Neural Networks (GNNs) are powerful models designed for graph data...

5 Sina Sajadmanesh, et al. ∙

research

∙ 02/23/2022

Differentially Private Speaker Anonymization

Sharing real-world speech utterances is key to the training and deployme...

2 Ali Shahin Shamsabadi, et al. ∙

research

∙ 12/23/2021

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

Imagine a group of citizens willing to collectively contribute their per...

0 Riad Ladjel, et al. ∙

research

∙ 11/17/2021

Differentially Private Federated Learning on Heterogeneous Data

Federated Learning (FL) is a paradigm for large-scale distributed learni...

0 Maxence Noble, et al. ∙

research

∙ 10/22/2021

Differentially Private Coordinate Descent for Composite Empirical Risk Minimization

Machine learning models can leak information about the data used to trai...

0 Paul Mangold, et al. ∙

research

∙ 08/23/2021

Federated Multi-Task Learning under a Mixture of Distributions

The increasing size of data generated by smartphones and IoT devices mot...

14 Othmane Marfoq, et al. ∙

research

∙ 04/15/2021

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

The convergence speed of machine learning models trained with Federated ...

6 Aurélien Bellet, et al. ∙

research

∙ 12/09/2020

Privacy Amplification by Decentralization

Analyzing data owned by several parties while achieving a good trade-off...

0 Edwige Cyffers, et al. ∙

research

∙ 06/12/2020

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

Learning from data owned by several parties, as in federated learning, r...

0 César Sabater, et al. ∙

research

∙ 05/18/2020

Design Choices for X-vector Based Speaker Anonymization

The recently proposed x-vector based anonymization scheme converts any i...

0 Brij Mohan Lal Srivastava, et al. ∙

research

∙ 02/19/2020

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

Many applications of artificial intelligence, ranging from credit lendin...

0 Robin Vogel, et al. ∙

research

∙ 12/10/2019

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients...

33 Peter Kairouz, et al. ∙

research

∙ 11/12/2019

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

Automatic speech recognition (ASR) is a key technology in many services ...

0 Brij Mohan Lal Srivastava, et al. ∙

research

∙ 11/10/2019

Evaluating Voice Conversion-based Privacy Protection against Informed Attackers

Speech signals are a rich source of speaker-related information includin...

0 Brij Mohan Lal Srivastava, et al. ∙

research

∙ 10/09/2019

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

In this paper, we study the problem of computing U-statistics of degree ...

0 James Bell, et al. ∙

research

∙ 08/13/2019

metric-learn: Metric Learning Algorithms in Python

metric-learn is an open source Python package implementing supervised an...

0 William de Vazelhes, et al. ∙

research

∙ 06/21/2019

Trade-offs in Large-Scale Distributed Tuplewise Estimation and Learning

The development of cluster computing frameworks has allowed practitioner...

2 Robin Vogel, et al. ∙

research

∙ 02/19/2019

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

Gossip protocols, also called rumor spreading or epidemic protocols, are...

0 Aurélien Bellet, et al. ∙

research

∙ 01/24/2019

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

We study the decentralized machine learning scenario where many users co...

0 Valentina Zantedeschi, et al. ∙

research

∙ 07/20/2018

Escaping the Curse of Dimensionality in Similarity Learning: Efficient Frank-Wolfe Algorithm and Generalization Bounds

Similarity and metric learning provides a principled approach to constru...

0 Kuan Liu, et al. ∙

research

∙ 07/18/2018

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

The performance of many machine learning techniques depends on the choic...

0 Robin Vogel, et al. ∙

research

∙ 03/27/2018

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

The amount of personal data collected in our everyday interactions with ...

0 Pierre Dellenbach, et al. ∙

research

∙ 12/20/2017

A Distributed Frank-Wolfe Framework for Learning Low-Rank Matrices with the Trace Norm

We consider the problem of learning a high-dimensional but low-rank matr...

0 Wenjie Zheng, et al. ∙

research

∙ 05/23/2017

Fast and Differentially Private Algorithms for Decentralized Collaborative Machine Learning

Consider a set of agents in a peer-to-peer communication network, where ...

0 Aurélien Bellet, et al. ∙

research

∙ 01/13/2017

Kernel Approximation Methods for Speech Recognition

We study large-scale kernel methods for acoustic modeling in speech reco...

0 Avner May, et al. ∙

research

∙ 10/17/2016

Decentralized Collaborative Learning of Personalized Models over Networks

We consider a set of learning agents in a collaborative peer-to-peer net...

0 Paul Vanhaesebrouck, et al. ∙

research

∙ 06/08/2016

Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions

In decentralized networks (of sensors, connected objects, etc.), there i...

0 Igor Colin, et al. ∙

research

∙ 03/18/2016

A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition

We study large-scale kernel methods for acoustic modeling and compare to...

0 Zhiyun Lu, et al. ∙

research

∙ 11/17/2015

Extending Gossip Algorithms to Distributed Estimation of U-Statistics

Efficient and robust algorithms for decentralized estimation in networks...

0 Igor Colin, et al. ∙

research

∙ 01/12/2015

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

In a wide range of statistical learning problems such as ranking, cluste...

0 Stephan Clémençon, et al. ∙

research

∙ 11/14/2014

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

The computational complexity of kernel methods has often been a major ba...

0 Zhiyun Lu, et al. ∙

research

∙ 11/10/2014

Similarity Learning for High-Dimensional Sparse Data

A good measure of similarity between data points is crucial to many task...

0 Kuan Liu, et al. ∙

research

∙ 04/15/2014

Sparse Compositional Metric Learning

We propose a new approach for metric learning by framing it as learning ...

0 Yuan Shi, et al. ∙

research

∙ 04/09/2014

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

Learning sparse combinations is a frequent theme in machine learning. In...

0 Aurélien Bellet, et al. ∙

research

∙ 07/17/2013

Supervised Metric Learning with Generalization Guarantees

The crucial importance of metrics in machine learning algorithms has led...

0 Aurélien Bellet, et al. ∙

research

∙ 06/28/2013

A Survey on Metric Learning for Feature Vectors and Structured Data

The need for appropriate ways to measure the distance or similarity betw...

1 Aurélien Bellet, et al. ∙

research

∙ 09/05/2012

Robustness and Generalization for Metric Learning

Metric learning has attracted a lot of interest over the last decade, bu...

0 Aurélien Bellet, et al. ∙

research

∙ 06/27/2012

Similarity Learning for Provably Accurate Sparse Linear Classification

In recent years, the crucial importance of metrics in machine learning a...

0 Aurélien Bellet, et al. ∙

Aurélien Bellet

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