Risi Kondor

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Citations	7768	4551
h-index	24	21
i10-index	34	27

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Tony JebaraSpotify & Columbia UniversityVerified email at spotify.com
Gabor CsanyiProfessor of Molecular Modelling, Engineering Laboratory, University of CambridgeVerified email at cam.ac.uk
Albert P. BartokUniversity of WarwickVerified email at warwick.ac.uk
Shubhendu TrivediMassachusetts Institute of TechnologyVerified email at csail.mit.edu
Karsten BorgwardtProfessor, ETH ZurichVerified email at bsse.ethz.ch
Vikas SinghUniversity of Wisconsin MadisonVerified email at biostat.wisc.edu
S V N VishwanathanAssociate Professor of Statistics and Computer Science, Purdue UniversityVerified email at stat.purdue.edu
Deepti PachauriResearch ScientistVerified email at cs.wisc.edu
Alex SmolaAmazon Web ServicesVerified email at smola.org
Nedelina TenevaUniversity of ChicagoVerified email at uchicago.edu
Pramod Kaushik MudrakartaGoogle ResearchVerified email at google.com
Vikas K. GargMassachusetts Institute of Technology (MIT)Verified email at mit.edu
Marconi BarbosaThe Eccles Institute of Neuroscience, John Curtin School of Medical Research, The AustralianVerified email at anu.edu.au
Jonathan Eskreis-WinklerEtsyVerified email at etsy.com
Walter DempseyHarvard UniversityVerified email at fas.harvard.edu
Jean-Philippe VertGoogle Brain / Mines ParisTechVerified email at mines-paristech.fr
Maxwell D. CollinsGoogleVerified email at google.com
Vamsi Krishna IthapuResearch Scientist, Facebook Reality LabsVerified email at fb.com
Yi DingMassachusetts Institute of TechnologyVerified email at csail.mit.edu
Ioannis KevrekidisJohns Hopkins UniversityVerified email at jhu.edu

Risi Kondor

Associate Professor, The University of Chicago

Verified email at cs.uchicago.edu - Homepage

Machine Learning Computational Harmonic Analysis Deep Learning Machine Learning for Physics


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Diffusion kernels on graphs and other discrete structures RI Kondor, J Lafferty Proceedings of the 19th international conference on machine learning 2002 …, 2002	1412	2002
Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons AP Bartók, MC Payne, R Kondor, G Csányi Physical review letters 104 (13), 136403, 2010	1098	2010
Graph kernels SVN Vishwanathan, NN Schraudolph, R Kondor, KM Borgwardt Journal of Machine Learning Research 11, 1201-1242, 2010	939	2010
Kernels and regularization on graphs AJ Smola, R Kondor Learning theory and kernel machines, 144-158, 2003	922	2003
On representing chemical environments AP Bartók, R Kondor, G Csányi Physical Review B 87 (18), 184115, 2013	846	2013
Probability product kernels T Jebara, R Kondor, A Howard The Journal of Machine Learning Research 5, 819-844, 2004	611	2004
A kernel between sets of vectors R Kondor, T Jebara Proceedings of the 20th international conference on machine learning (ICML …, 2003	337	2003
On the generalization of equivariance and convolution in neural networks to the action of compact groups R Kondor, S Trivedi International Conference on Machine Learning, 2747-2755, 2018	162	2018
Bhattacharyya and expected likelihood kernels T Jebara, R Kondor Learning theory and kernel machines, 57-71, 2003	161	2003
Solving the multi-way matching problem by permutation synchronization D Pachauri, R Kondor, V Singh Advances in neural information processing systems, 1860-1868, 2013	143	2013
The multiscale laplacian graph kernel R Kondor, H Pan arXiv preprint arXiv:1603.06186, 2016	112	2016
Multi-object tracking with representations of the symmetric group R Kondor, A Howard, T Jebara Artificial Intelligence and Statistics, 211-218, 2007	94	2007
Group theoretical methods in machine learning IR Kondor Columbia University, 2008	87	2008
The graphlet spectrum R Kondor, N Shervashidze, KM Borgwardt Proceedings of the 26th Annual International Conference on Machine Learning …, 2009	81	2009
Clebsch-gordan nets: a fully fourier space spherical convolutional neural network R Kondor, Z Lin, S Trivedi arXiv preprint arXiv:1806.09231, 2018	80	2018
Covariant compositional networks for learning graphs R Kondor, HT Son, H Pan, B Anderson, S Trivedi arXiv preprint arXiv:1801.02144, 2018	79	2018
The skew spectrum of graphs R Kondor, KM Borgwardt Proceedings of the 25th international conference on Machine learning, 496-503, 2008	79	2008
Graph kernels SVN Vishwanathan, KM Borgwardt, IR Kondor, NN Schraudolph arXiv preprint arXiv:0807.0093, 2008	70	2008
Cormorant: Covariant molecular neural networks B Anderson, TS Hy, R Kondor arXiv preprint arXiv:1906.04015, 2019	62	2019
N-body networks: a covariant hierarchical neural network architecture for learning atomic potentials R Kondor arXiv preprint arXiv:1803.01588, 2018	49	2018

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