• Publications
  • Influence
A Survey on Metric Learning for Feature Vectors and Structured Data
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 forExpand
  • 500
  • 30
  • PDF
Advances and Open Problems in Federated Learning
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
  • 257
  • 30
  • PDF
Sparse Compositional Metric Learning
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 flexibleExpand
  • 67
  • 19
  • PDF
A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning
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 notExpand
  • 31
  • 7
  • PDF
Robustness and generalization for metric learning
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 anExpand
  • 53
  • 4
  • PDF
Similarity Learning for Provably Accurate Sparse Linear Classification
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 artExpand
  • 50
  • 4
  • PDF
Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions
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 theExpand
  • 43
  • 4
  • PDF
Personalized and Private Peer-to-Peer Machine Learning
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 modelsExpand
  • 30
  • 4
  • PDF
Kernel Approximation Methods for Speech Recognition
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 recognitionExpand
  • 25
  • 3
  • PDF
Similarity Learning for High-Dimensional Sparse Data
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 notExpand
  • 22
  • 2
  • PDF