431 citations · 1.2k across the 62 of their papers we have counts for
6 papers · 1 filter
Fair Densities via Boosting the Sufficient Statistics of Exponential Families
Alexander Soen, Hisham Husain, Richard Nock
We introduce a boosting algorithm to pre-process data for fairness. Starting from an initial fair but inaccurate distribution, our approach shifts towards better data fitting while…
All your loss are belong to Bayes
Christian Walder, Richard Nock
Loss functions are a cornerstone of machine learning and the starting point of most algorithms. Statistics and Bayesian decision theory have contributed, via properness, to elicit…
Cumulant-free closed-form formulas for some common (dis)similarities between densities of an exponential family
Frank Nielsen, Richard Nock
It is well-known that the Bhattacharyya, Hellinger, Kullback-Leibler, -divergences, and Jeffreys' divergences between densities belonging to a same exponential family have gener…
Generalised Lipschitz Regularisation Equals Distributional Robustness
Zac Cranko, Zhan Shi, Xinhua Zhang +2
The problem of adversarial examples has highlighted the need for a theory of regularisation that is general enough to apply to exotic function classes, such as universal approximat…
Supervised Learning: No Loss No Cry
Richard Nock, Aditya Krishna Menon
Supervised learning requires the specification of a loss function to minimise. While the theory of admissible losses from both a computational and statistical perspective is well-d…
Boosted and Differentially Private Ensembles of Decision Trees
Richard Nock, Wilko Henecka
Boosted ensemble of decision tree (DT) classifiers are extremely popular in international competitions, yet to our knowledge nothing is formally known on how to make them \textit{a…