431 citations · 1.2k across the 62 of their papers we have counts for
9 papers · 1 filter
The Bregman chord divergence
Frank Nielsen, Richard Nock
Distances are fundamental primitives whose choice significantly impacts the performances of algorithms in machine learning and signal processing. However selecting the most appropr…
Lipschitz Networks and Distributional Robustness
Zac Cranko, Simon Kornblith, Zhan Shi +1
Robust risk minimisation has several advantages: it has been studied with regards to improving the generalisation properties of models and robustness to adversarial perturbation. W…
Hyperparameter Learning for Conditional Kernel Mean Embeddings with Rademacher Complexity Bounds
Kelvin Hsu, Richard Nock, Fabio Ramos
Conditional kernel mean embeddings are nonparametric models that encode conditional expectations in a reproducing kernel Hilbert space. While they provide a flexible and powerful f…
D-PAGE: Diverse Paraphrase Generation
Qiongkai Xu, Juyan Zhang, Lizhen Qu +2
In this paper, we investigate the diversity aspect of paraphrase generation. Prior deep learning models employ either decoding methods or add random input noise for varying outputs…
Private Text Classification
Leif W. Hanlen, Richard Nock, Hanna Suominen +1
Confidential text corpora exist in many forms, but do not allow arbitrary sharing. We explore how to use such private corpora using privacy preserving text analytics. We construct…
Integral Privacy for Sampling
Hisham Husain, Zac Cranko, Richard Nock
Differential privacy is a leading protection setting, focused by design on individual privacy. Many applications, in medical / pharmaceutical domains or social networks, rather pos…