Dino Sejdinovic

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Arthur GrettonGatsby Computational Neuroscience Unit, UCLVerified 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
Dejan VukobratovicUniversity of Novi SadVerified email at uns.ac.rs
Angela DoufexiProfessor, University of BristolVerified email at bristol.ac.uk
Bharath SriperumbudurPennsylvania State UniversityVerified email at psu.edu
Michael A OsborneProfessor of Machine Learning, University of OxfordVerified email at robots.ox.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 TonUniversity of OxfordVerified email at spc.ox.ac.uk
Mark GirolamiUniversity of CambridgeVerified email at cam.ac.uk
Wittawat JitkrittumGoogle ResearchVerified email at google.com
Vladimir StankovicReader at University of StrathclydeVerified email at strath.ac.uk
Lina StankovicSenior Lecturer of Electronic Engineering, University of StrathclydeVerified email at strath.ac.uk
Sarah FilippiSenior Lecturer, Imperial College LondonVerified email at imperial.ac.uk
Chris. J. OatesNewcastle UniversityVerified email at ncl.ac.uk
François-Xavier BriolLecturer in Statistical Science, UCLVerified email at turing.ac.uk
Zixiang XiongProf. Texas A&M UniversityVerified email at ece.tamu.edu

Dino Sejdinovic

Associate Professor, University of Oxford

Verified email at stats.ox.ac.uk - Homepage

Statistical Inference Machine Learning Information Theory


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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	510	2013
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	485	2012
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	295	2019
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	227	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	227	2007
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	166	2018
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	166	2009
Probabilistic Integration: A Role in Statistical Computation? FX Briol, CJ Oates, M Girolami, MA Osborne, D Sejdinovic Statistical Science 34 (1), 1-22, 2019	157*	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	121	2015
Large-scale kernel methods for independence testing Q Zhang, S Filippi, A Gretton, D Sejdinovic Statistics and Computing 28 (1), 113–130, 2018	96	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	81	2019
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	80	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	78	2015
K2-ABC: Approximate Bayesian Computation with Kernel Embeddings M Park, W Jitkrittum, D Sejdinovic AISTATS, 2015	77	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	72	2009
Temporal structure in associative retrieval Z Kurth-Nelson, G Barnes, D Sejdinovic, R Dolan, P Dayan Elife 4, e04919, 2015	64	2015
A wild bootstrap for degenerate kernel tests KP Chwialkowski, D Sejdinovic, A Gretton Advances in neural information processing systems 27, 3608-3616, 2014	60	2014
Hamiltonian variational auto-encoder AL Caterini, A Doucet, D Sejdinovic Advances in Neural Information Processing Systems 31, 8167-8177, 2018	55	2018
Kernel Adaptive Metropolis-Hastings D Sejdinovic, H Strathmann, ML Garcia, C Andrieu, A Gretton International Conference on Machine Learning (ICML) - JMLR W&CP 32(2), 1665 …, 2014	48	2014
Bayesian sequential compressed sensing in sparse dynamical systems D Sejdinovic, C Andrieu, R Piechocki Proc. 48th Annual Allerton 2010 Conf. on Communication, Control and …, 2010	46	2010

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