Dino Sejdinovic

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Arthur GrettonGatsby Computational Neuroscience Unit and DeepmindVerified email at gatsby.ucl.ac.uk
Seth Flaxmanassociate professor at OxfordVerified email at cs.ox.ac.uk
Robert PiechockiUniversity of BristolVerified email at bristol.ac.uk
Kenji FukumizuThe Institute of Statistical MathematicsVerified email at ism.ac.jp
Bharath SriperumbudurPennsylvania State UniversityVerified email at psu.edu
Michael A OsborneProfessor of Machine Learning, University of OxfordVerified email at robots.ox.ac.uk
Dejan VukobratovicUniversity of Novi SadVerified email at uns.ac.rs
Angela DoufexiProfessor, University of BristolVerified email at bristol.ac.uk
Heiko StrathmannResearch Scientist at DeepmindVerified email at deepmind.com
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindVerified email at stats.ox.ac.uk
Natalia AresOxford UniversityVerified email at eng.ox.ac.uk
Jean-Francois TonByteDance ResearchVerified email at bytedance.com
Mark GirolamiUniversity of CambridgeVerified email at cam.ac.uk
Chau Siu LunPostdoc at CISPA - Helmholtz Center for Information SecurityVerified email at cispa.de
Chris. J. OatesNewcastle UniversityVerified email at ncl.ac.uk
François-Xavier BriolAssociate Professor in Statistical Science, UCLVerified email at turing.ac.uk
Sarah FilippiSenior Lecturer, Imperial College LondonVerified email at imperial.ac.uk
Wittawat JitkrittumGoogle ResearchVerified email at google.com
Ho Chung Leon LawDPhil in Statistics, University Of OxfordVerified email at spc.ox.ac.uk
Vladimir StankovicProfessor at University of StrathclydeVerified email at strath.ac.uk

Dino Sejdinovic

Professor, University of Adelaide

Verified email at adelaide.edu.au - Homepage

Statistical Inference Machine Learning Information Theory


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Optimal kernel choice for large-scale two-sample tests A Gretton, BK Sriperumbudur, D Sejdinovic, H Strathmann, ... Advances in Neural Information Processing Systems 25, 1214-1222, 2012	664	2012
Equivalence of distance-based and RKHS-based statistics in hypothesis testing D Sejdinovic, B Sriperumbudur, A Gretton, K Fukumizu The Annals of Statistics 41 (5), 2263–2291, 2013	655	2013
Detecting and quantifying causal associations in large nonlinear time series datasets J Runge, P Nowack, M Kretschmer, S Flaxman, D Sejdinovic Science Advances 5 (11), eaau4996, 2019	518	2019
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences M Kanagawa, P Hennig, D Sejdinovic, BK Sriperumbudur arXiv preprint arXiv:1807.02582, 2018	276	2018
Expanding window fountain codes for unequal error protection D Sejdinovic, D Vukobratovic, A Doufexi, V Senk, R Piechocki Communications, IEEE Transactions on 57 (9), 2510-2516, 2009	232	2009
Expanding window fountain codes for unequal error protection D Sejdinovic, D Vukobratovic, A Doufexi, V Senk, R Piechocki Asilomar Conference on Signals, Systems and Computers, 1020–1024, 2007	232	2007
Probabilistic Integration: A Role in Statistical Computation? FX Briol, CJ Oates, M Girolami, MA Osborne, D Sejdinovic Statistical Science 34 (1), 1-22, 2019	197*	2019
Scalable video multicast using expanding window fountain codes D Vukobratovic, V Stankovic, D Sejdinovic, L Stankovic, Z Xiong IEEE Transactions on Multimedia 11 (6), 1094-1104, 2009	168	2009
Fast two-sample testing with analytic representations of probability measures KP Chwialkowski, A Ramdas, D Sejdinovic, A Gretton Advances in Neural Information Processing Systems 28, 1981-1989, 2015	160	2015
Unrepresentative big surveys significantly overestimate US vaccine uptake V Bradley, S Kuriwaki, M Isakov, D Sejdinovic, XL Meng, S Flaxman Nature, 2021	153	2021
Large-scale kernel methods for independence testing Q Zhang, S Filippi, A Gretton, D Sejdinovic Statistics and Computing 28 (1), 113–130, 2018	126	2018
Towards a Unified Analysis of Random Fourier Features Z Li, JF Ton, D Oglic, D Sejdinovic International Conference on Machine Learning, 3905-3914, 2019	116	2019
K2-ABC: Approximate Bayesian Computation with Kernel Embeddings M Park, W Jitkrittum, D Sejdinovic AISTATS, 2015	94	2015
Note on noisy group testing: asymptotic bounds and belief propagation reconstruction D Sejdinovic, O Johnson Proc. 48th Annual Allerton 2010 Conf. on Communication, Control and …, 2010	94	2010
Hamiltonian variational auto-encoder AL Caterini, A Doucet, D Sejdinovic Advances in Neural Information Processing Systems 31, 8167-8177, 2018	87	2018
Gradient-free Hamiltonian Monte Carlo with efficient kernel exponential families H Strathmann, D Sejdinovic, S Livingstone, Z Szabo, A Gretton Advances in Neural Information Processing Systems 28, 955-963, 2015	87	2015
AND-OR tree analysis of distributed LT codes D Sejdinovic, RJ Piechocki, A Doufexi IEEE Information Theory Workshop on Networking and Information Theory (ITW …, 2009	77	2009
Temporal structure in associative retrieval Z Kurth-Nelson, G Barnes, D Sejdinovic, R Dolan, P Dayan Elife 4, e04919, 2015	76	2015
A wild bootstrap for degenerate kernel tests KP Chwialkowski, D Sejdinovic, A Gretton Advances in neural information processing systems 27, 3608-3616, 2014	68	2014
Machine learning enables completely automatic tuning of a quantum device faster than human experts H Moon, DT Lennon, J Kirkpatrick, NM van Esbroeck, LC Camenzind, ... Nature communications 11 (1), 4161, 2020	57	2020

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