431 citations · 1.2k across the 62 of their papers we have counts for
7 papers · 2 filters
Large Margin Nearest Neighbor Classification using Curved Mahalanobis Distances
Frank Nielsen, Boris Muzellec, Richard Nock
We consider the supervised classification problem of machine learning in Cayley-Klein projective geometries: We show how to learn a curved Mahalanobis metric distance corresponding…
Tsallis Regularized Optimal Transport and Ecological Inference
Boris Muzellec, Richard Nock, Giorgio Patrini +1
Optimal transport is a powerful framework for computing distances between probability distributions. We unify the two main approaches to optimal transport, namely Monge-Kantorovitc…
A scaled Bregman theorem with applications
Richard Nock, Aditya Krishna Menon, Cheng Soon Ong
Bregman divergences play a central role in the design and analysis of a range of machine learning algorithms. This paper explores the use of Bregman divergences to establish reduct…
The Crossover Process: Learnability and Data Protection from Inference Attacks
Richard Nock, Giorgio Patrini, Finnian Lattimore +1
It is usual to consider data protection and learnability as conflicting objectives. This is not always the case: we show how to jointly control inference --- seen as the attack ---…
Fast Learning from Distributed Datasets without Entity Matching
Giorgio Patrini, Richard Nock, Stephen Hardy +1
Consider the following data fusion scenario: two datasets/peers contain the same real-world entities described using partially shared features, e.g. banking and insurance company r…
k-variates++: more pluses in the k-means++
Richard Nock, Raphaël Canyasse, Roksana Boreli +1
k-means++ seeding has become a de facto standard for hard clustering algorithms. In this paper, our first contribution is a two-way generalisation of this seeding, k-variates++, th…
Loss factorization, weakly supervised learning and label noise robustness
Giorgio Patrini, Frank Nielsen, Richard Nock +1
We prove that the empirical risk of most well-known loss functions factors into a linear term aggregating all labels with a term that is label free, and can further be expressed by…