# Aurélien Bellet

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- Publications
- Influence

A Survey on Metric Learning for Feature Vectors and Structured Data

- Aurélien Bellet, Amaury Habrard, M. Sebban
- Mathematics, Computer Science
- ArXiv
- 27 June 2013

The need for appropriate ways to measure the distance or similarity between data is ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such good metrics for… Expand

Advances and Open Problems in Federated Learning

- Peter Kairouz, H. McMahan, +55 authors Sen Zhao
- Mathematics, Computer Science
- ArXiv
- 10 December 2019

Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g.… Expand

Sparse Compositional Metric Learning

- Y. Shi, Aurélien Bellet, F. Sha
- Computer Science, Mathematics
- AAAI
- 15 April 2014

We propose a new approach for metric learning by framing it as learning a sparse combination of locally discriminative metrics that are inexpensive to generate from the training data. This flexible… Expand

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

- Aurélien Bellet, Yingyu Liang, Alireza Bagheri Garakani, Maria-Florina Balcan, F. Sha
- Computer Science, Mathematics
- SDM
- 9 April 2014

Learning sparse combinations is a frequent theme in machine learning. In this paper, we study its associated optimization problem in the distributed setting where the elements to be combined are not… Expand

Robustness and generalization for metric learning

- Aurélien Bellet, Amaury Habrard
- Computer Science, Mathematics
- Neurocomputing
- 5 September 2012

Abstract Metric learning has attracted a lot of interest over the last decade, but the generalization ability of such methods has not been thoroughly studied. In this paper, we introduce an… Expand

Similarity Learning for Provably Accurate Sparse Linear Classification

- Aurélien Bellet, Amaury Habrard, M. Sebban
- Mathematics, Computer Science
- ICML
- 26 June 2012

In recent years, the crucial importance of metrics in machine learning algorithms has led to an increasing interest for optimizing distance and similarity functions. Most of the state of the art… Expand

Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions

- I. Colin, Aurélien Bellet, J. Salmon, S. Clémençon
- Computer Science, Mathematics
- ICML
- 8 June 2016

In decentralized networks (of sensors, connected objects, etc.), there is an important need for efficient algorithms to optimize a global cost function, for instance to learn a global model from the… Expand

Personalized and Private Peer-to-Peer Machine Learning

- Aurélien Bellet, R. Guerraoui, Mahsa Taziki, M. Tommasi
- Computer Science, Mathematics
- AISTATS
- 23 May 2017

The rise of connected personal devices together with privacy concerns call for machine learning algorithms capable of leveraging the data of a large number of agents to learn personalized models… Expand

Kernel Approximation Methods for Speech Recognition

- Avner May, Alireza Bagheri Garakani, +9 authors F. Sha
- Mathematics, Computer Science
- J. Mach. Learn. Res.
- 13 January 2017

We study large-scale kernel methods for acoustic modeling in speech recognition and compare their performance to deep neural networks (DNNs). We perform experiments on four speech recognition… Expand

Similarity Learning for High-Dimensional Sparse Data

- K. Liu, Aurélien Bellet, F. Sha
- Mathematics, Computer Science
- AISTATS
- 10 November 2014

A good measure of similarity between data points is crucial to many tasks in machine learning. Similarity and metric learning methods learn such measures automatically from data, but they do not… Expand