Risi Kondor

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Truong Son HyUniversity of California San DiegoVerified email at ucsd.edu
Tony JebaraSpotifyVerified 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 BorgwardtDirector, Max Planck Institute of BiochemistryVerified email at biochem.mpg.de
Horace PanUniversity of ChicagoVerified email at uchicago.edu
Brandon M AndersonAtomwise, Inc.Verified email at jfi.uchicago.edu
Vikas SinghUniversity of Wisconsin MadisonVerified email at biostat.wisc.edu
Andrew HowardGoogle ResearchVerified email at google.com
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 SmolaBoson AIVerified email at smola.org
Nedelina TenevaUniversity of ChicagoVerified email at uchicago.edu
Erik ThiedeResearch Fellow, Flatiron InstituteVerified email at flatironinstitute.org
Alexander BogatskiyFlatiron InstituteVerified email at flatironinstitute.org
Pramod Kaushik MudrakartaGoogle ResearchVerified email at google.com
Zhen LinUniversity of Illinois at Urbana-ChampaignVerified email at illinois.edu
Wenda ZhouNew York UniversityVerified email at nyu.edu
Vikas K. GargMassachusetts Institute of Technology (MIT)Verified email at mit.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


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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	1881	2010
On representing chemical environments AP Bartók, R Kondor, G Csányi Physical Review B 87 (18), 184115, 2013	1638	2013
Diffusion kernels on graphs and other discrete structures RI Kondor, J Lafferty Proceedings of the 19th international conference on machine learning 2002 …, 2002	1562	2002
Graph kernels SVN Vishwanathan, NN Schraudolph, R Kondor, KM Borgwardt Journal of Machine Learning Research 11, 1201-1242, 2010	1202	2010
Kernels and regularization on graphs AJ Smola, R Kondor Learning Theory and Kernel Machines: 16th Annual Conference on Learning …, 2003	1034	2003
Probability product kernels T Jebara, R Kondor, A Howard The Journal of Machine Learning Research 5, 819-844, 2004	660	2004
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	364	2018
A kernel between sets of vectors R Kondor, T Jebara Proceedings of the 20th international conference on machine learning (ICML …, 2003	355	2003
Cormorant: Covariant molecular neural networks B Anderson, TS Hy, R Kondor Advances in neural information processing systems 32, 2019	284	2019
Clebsch–gordan nets: a fully fourier space spherical convolutional neural network R Kondor, Z Lin, S Trivedi Advances in Neural Information Processing Systems 31, 2018	201	2018
Bhattacharyya and expected likelihood kernels T Jebara, R Kondor COLT, 57-71, 2003	186	2003
Solving the multi-way matching problem by permutation synchronization D Pachauri, R Kondor, V Singh Advances in neural information processing systems 26, 2013	178	2013
The multiscale laplacian graph kernel R Kondor, H Pan Advances in neural information processing systems 29, 2016	165	2016
Covariant compositional networks for learning graphs R Kondor, HT Son, H Pan, B Anderson, S Trivedi arXiv preprint arXiv:1801.02144, 2018	124	2018
Group theoretical methods in machine learning IR Kondor Columbia University, 2008	113	2008
Multi-object tracking with representations of the symmetric group R Kondor, A Howard, T Jebara Artificial Intelligence and Statistics, 211-218, 2007	104	2007
N-body networks: a covariant hierarchical neural network architecture for learning atomic potentials R Kondor arXiv preprint arXiv:1803.01588, 2018	96	2018
The graphlet spectrum R Kondor, N Shervashidze, KM Borgwardt Proceedings of the 26th Annual International Conference on Machine Learning …, 2009	89	2009
Lorentz group equivariant neural network for particle physics A Bogatskiy, B Anderson, J Offermann, M Roussi, D Miller, R Kondor International Conference on Machine Learning, 992-1002, 2020	85	2020
The skew spectrum of graphs R Kondor, KM Borgwardt Proceedings of the 25th international conference on Machine learning, 496-503, 2008	82	2008

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