Mark D. Reid

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Robert C. WilliamsonUniversity of TübingenVerified email at uni-tuebingen.de
Rafael FrongilloComputer Science, CU BoulderVerified email at colorado.edu
Tim van ErvenAssociate professor at the University of Amsterdam, the NetherlandsVerified email at uva.nl
Nicolás Della PennaGroupLangVerified email at grouplang.com
Tiberio CaetanoGradient InstituteVerified email at gradientinstitute.org
Nishant A. MehtaAssistant Professor, University of VictoriaVerified email at uvic.ca
Peter GrünwaldGroup Leader Machine Learning, CWI; Professor of Statistics, Leiden UniversityVerified email at cwi.nl
Malcolm RyanSchool of Computing, Macquarie UniversityVerified email at mq.edu.au
Jie ZhouProfessor, Department of Automation, Tsinghua UniversityVerified email at tsinghua.edu.cn
Darío García-GarcíaFeverVerified email at feverup.com
James PettersonPrincipal Data Scientist, IAGVerified email at nicta.com.au
Professor Javen Qinfeng ShiDirector for Causal AI Group, University of AdelaideVerified email at adelaide.edu.au
Kristian KerstingProfessor of AI & ML, Technical University of Darmstadt, Co-Director hessian.ai, DFKI, CAIRNE, ELLISVerified email at cs.tu-darmstadt.de
Wray BuntineProfessor, VinUniversityVerified email at vinuni.edu.vn
Novi QuadriantoProfessor of Machine Learning, University of Sussex UK, BCAM Spain, Monash IndonesiaVerified email at sussex.ac.uk
Anne Mindika PremachandraLecturer, University of Colombo School of ComputingVerified email at ucsc.cmb.ac.lk
James MontgomeryUniversity of TasmaniaVerified email at utas.edu.au
Anton van den HengelDirector of the Centre for Augmented Reasoning at AIML.Verified email at adelaide.edu.au
David BalduzziXTX MarketsVerified email at google.com
Shalmali JoshiColumbia UniversityVerified email at cumc.columbia.edu

Mark D. Reid

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Machine Learning Online Learning Prediction Markets Information Theory


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Composite binary losses MD Reid, RC Williamson The Journal of Machine Learning Research 9999, 2387-2422, 2010	265	2010
Information, divergence and risk for binary experiments MD Reid, RC Williamson Journal of Machine Learning Research 12, 731-817, 2011	223	2011
Fast rates in statistical and online learning T van Erven, PD Grünwald, NA Mehta, MD Reid, RC Williamson Journal of Machine Learning Research 16 (Sep), 1793−1861, 2015	122	2015
Surrogate regret bounds for proper losses MD Reid, RC Williamson Proceedings of the 26th Annual International Conference on Machine Learning …, 2009	66	2009
Generalised pinsker inequalities MD Reid, RC Williamson The 22nd Annual Conference on Learning Theory (COLT 2009), 2009	64	2009
Composite multiclass losses RC Williamson, E Vernet, MD Reid Journal of Machine Learning Research 17 (222), 1-52, 2016	62	2016
Composite Multiclass Losses E Vernet, RC Williamson, MD Reid Neural Information Processing Systems, 2011	60	2011
Tighter Variational Representations of f-Divergences via Restriction to Probability Measures A Ruderman, MD Reid, D García-García, J Petterson International Conference on Machine Learning, 2012	59	2012
Kernel conditional quantile estimation via reduction revisited N Quadrianto, K Kersting, MD Reid, TS Caetano, WL Buntine 2009 Ninth IEEE International Conference on Data Mining, 938-943, 2009	42	2009
Interpreting prediction markets: a stochastic approach R Frongillo, N Della Penna, M Reid Advances in Neural Information Processing Systems 25, 3275-3283, 2012	34	2012
Gaussian Distribution. X Zhang Encyclopedia of Machine Learning, 425-428, 2010	31*	2010
An Improved Multiclass LogitBoost Using Adaptive-One-vs-One P Sun, MD Reid, J Zhou Machine Learning Journal (To Appear), 2014	29	2014
Mixability is Bayes risk curvature relative to log loss T Erven, MD Reid, RC Williamson Proceedings of the 24th Annual Conference on Learning Theory, 233-252, 2011	28	2011
Learning to fly: An application of hierarchical reinforcement learning MRK Ryan, M Reid Proceedings of the Seventeenth International Conference on Machine Learning …, 2000	28	2000
Crowd & Prejudice: An Impossibility Theorem for Crowd Labelling without a Gold Standard N Della Penna, MD Reid Proceedings of Collective Intelligence, 2012	23	2012
Convergence analysis of prediction markets via randomized subspace descent R Frongillo, MD Reid Advances in Neural Information Processing Systems 28, 2015	22	2015
Generalized mixability via entropic duality MD Reid, RM Frongillo, RC Williamson, N Mehta Conference on Learning Theory, 1501-1522, 2015	19	2015
Mixability in Statistical Learning T van Erven, PD Grünwald, MD Reid, RC Williamson Neural Information Processing Systems (NIPS), 2012	18*	2012
Using ILP to improve planning in hierarchical reinforcement learning M Reid, M Ryan International Conference on Inductive Logic Programming, 174-190, 2000	18	2000
AOSO-LogitBoost: Adaptive One-Vs-One LogitBoost for Multi-Class Problems P Sun, MD Reid, J Zhou International Conference on Machine Learning, 2012	14	2012

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