Dino Sejdinovic

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Arthur GrettonGatsby Computational Neuroscience Unit and Google 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
Angela DoufexiProfessor, University of BristolVerified email at bristol.ac.uk
Heiko StrathmannResearch Scientist at DeepmindVerified email at deepmind.com
Michael A OsborneProfessor of Machine Learning, University of OxfordVerified email at robots.ox.ac.uk
Dejan VukobratovicICONIC Centre, Faculty of Technical Sciences, University of Novi SadVerified email at uns.ac.rs
Chau Siu LunPostdoc at CISPA - Helmholtz Center for Information SecurityVerified email at cispa.de
Natalia AresOxford UniversityVerified email at eng.ox.ac.uk
Zhu Li (Michael)University College LondonVerified email at ucl.ac.uk
Jean-Francois TonByteDance ResearchVerified email at bytedance.com
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindVerified email at stats.ox.ac.uk
Mark GirolamiUniversity of CambridgeVerified email at cam.ac.uk
Jakob RungeTU Dresden / ScaDS.AIVerified email at tu-dresden.de
Sarah FilippiReader, 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
Motonobu KanagawaEURECOMVerified email at eurecom.fr

Dino Sejdinovic

Professor of Statistical Machine Learning, University of Adelaide

Verified email at adelaide.edu.au - Homepage

Machine Learning Statistical Inference Information Theory


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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	890	2019
Optimal kernel choice for large-scale two-sample tests A Gretton, D Sejdinovic, H Strathmann, S Balakrishnan, M Pontil, ... Advances in Neural Information Processing Systems 25, 1214-1222, 2012	844	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	819	2013
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	420	2018
Unrepresentative big surveys significantly overestimated US vaccine uptake V Bradley, S Kuriwaki, M Isakov, D Sejdinovic, XL Meng, S Flaxman Nature, 2021	256	2021
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	242	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	242	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	218*	2019
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	185	2015
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	173	2009
Towards a Unified Analysis of Random Fourier Features Z Li, JF Ton, D Oglic, D Sejdinovic International Conference on Machine Learning, 3905-3914, 2019	154	2019
Large-scale kernel methods for independence testing Q Zhang, S Filippi, A Gretton, D Sejdinovic Statistics and Computing 28 (1), 113–130, 2018	147	2018
K2-ABC: Approximate Bayesian Computation with Kernel Embeddings M Park, W Jitkrittum, D Sejdinovic AISTATS, 2015	117	2015
Hamiltonian variational auto-encoder AL Caterini, A Doucet, D Sejdinovic Advances in Neural Information Processing Systems 31, 8167-8177, 2018	112	2018
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	102	2010
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	94	2015
Temporal structure in associative retrieval Z Kurth-Nelson, G Barnes, D Sejdinovic, R Dolan, P Dayan Elife 4, e04919, 2015	88	2015
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	85	2020
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	78	2009
A wild bootstrap for degenerate kernel tests KP Chwialkowski, D Sejdinovic, A Gretton Advances in neural information processing systems 27, 3608-3616, 2014	75	2014

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