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Data Preprocessing to Mitigate Bias with Boosted Fair Mollifiers


Dec 01, 2020
Alexander Soen, Hisham Husain, Richard Nock


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All your loss are belong to Bayes


Jun 08, 2020
Christian Walder, Richard Nock


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Cumulant-free closed-form formulas for some common (dis)similarities between densities of an exponential family


Apr 07, 2020
Frank Nielsen, Richard Nock

* 33 pages 

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Generalised Lipschitz Regularisation Equals Distributional Robustness


Feb 11, 2020
Zac Cranko, Zhan Shi, Xinhua Zhang, Richard Nock, Simon Kornblith


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Supervised Learning: No Loss No Cry


Feb 10, 2020
Richard Nock, Aditya Krishna Menon


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Boosted and Differentially Private Ensembles of Decision Trees


Feb 03, 2020
Richard Nock, Wilko Henecka


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Advances and Open Problems in Federated Learning


Dec 10, 2019
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao


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Proper-Composite Loss Functions in Arbitrary Dimensions


Feb 19, 2019
Zac Cranko, Robert C. Williamson, Richard Nock


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Adversarial Networks and Autoencoders: The Primal-Dual Relationship and Generalization Bounds


Feb 03, 2019
Hisham Husain, Richard Nock, Robert C. Williamson


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New Tricks for Estimating Gradients of Expectations


Jan 31, 2019
Christian J. Walder, Richard Nock, Cheng Soon Ong, Masashi Sugiyama


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The Bregman chord divergence


Oct 22, 2018
Frank Nielsen, Richard Nock

* 10 pages 

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Hyperparameter Learning for Conditional Mean Embeddings with Rademacher Complexity Bounds


Sep 19, 2018
Kelvin Hsu, Richard Nock, Fabio Ramos

* Best Student Machine Learning Paper Award Winner at ECML-PKDD 2018 (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases) 

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Monge beats Bayes: Hardness Results for Adversarial Training


Sep 12, 2018
Zac Cranko, Aditya Krishna Menon, Richard Nock, Cheng-Soon Ong, Zhan Shi, Christian Walder


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Integral Privacy for Sampling from Mollifier Densities with Approximation Guarantees


Sep 12, 2018
Hisham Husain, Zac Cranko, Richard Nock


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Lipschitz Networks and Distributional Robustness


Sep 04, 2018
Zac Cranko, Simon Kornblith, Zhan Shi, Richard Nock


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D-PAGE: Diverse Paraphrase Generation


Aug 13, 2018
Qiongkai Xu, Juyan Zhang, Lizhen Qu, Lexing Xie, Richard Nock


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Private Text Classification


Jun 19, 2018
Leif W. Hanlen, Richard Nock, Hanna Suominen, Neil Bacon

* 10 pages, 3 figures 

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Boosted Density Estimation Remastered


Jun 18, 2018
Zac Cranko, Richard Nock

* Contains lots of essential info 

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Entity Resolution and Federated Learning get a Federated Resolution


Mar 20, 2018
Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Giorgio Patrini, Guillaume Smith, Brian Thorne

* arXiv admin note: text overlap with arXiv:1711.10677 

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Evolving a Vector Space with any Generating Set


Dec 31, 2017
Richard Nock, Frank Nielsen


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Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption


Nov 29, 2017
Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, Brian Thorne


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On $w$-mixtures: Finite convex combinations of prescribed component distributions


Aug 02, 2017
Frank Nielsen, Richard Nock

* 25 pages 

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f-GANs in an Information Geometric Nutshell


Jul 14, 2017
Richard Nock, Zac Cranko, Aditya Krishna Menon, Lizhen Qu, Robert C. Williamson


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Generalizing Jensen and Bregman divergences with comparative convexity and the statistical Bhattacharyya distances with comparable means


May 03, 2017
Frank Nielsen, Richard Nock

* 24 pages 

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Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach


Mar 22, 2017
Giorgio Patrini, Alessandro Rozza, Aditya Menon, Richard Nock, Lizhen Qu

* Oral paper at CVPR 2017 

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The Crossover Process: Learnability and Data Protection from Inference Attacks


Mar 07, 2017
Richard Nock, Giorgio Patrini, Finnian Lattimore, Tiberio Caetano


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Semi-parametric Network Structure Discovery Models


Feb 27, 2017
Amir Dezfouli, Edwin V. Bonilla, Richard Nock


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A series of maximum entropy upper bounds of the differential entropy


Dec 09, 2016
Frank Nielsen, Richard Nock

* 18 pages 

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