Risi Kondor

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Gabor CsanyiProfessor of Molecular Modelling, Engineering Laboratory, University of CambridgeVerified email at cam.ac.uk
Shubhendu TrivediMassachusetts Institute of TechnologyVerified email at csail.mit.edu
Tony JebaraSpotifyVerified email at spotify.com
Albert P. BartokUniversity of WarwickVerified email at warwick.ac.uk
Brandon M AndersonAtomwise, Inc.Verified email at jfi.uchicago.edu
Karsten BorgwardtDirector, Max Planck Institute of BiochemistryVerified email at biochem.mpg.de
Horace PanUniversity of ChicagoVerified email at 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
Erik ThiedeAsst. Prof of Chemistry and Chemical Biology, Cornell UniversityVerified email at cornell.edu
Alexander BogatskiyFlatiron InstituteVerified email at flatironinstitute.org
Nedelina TenevaUniversity of ChicagoVerified email at uchicago.edu
Pramod Kaushik MudrakartaGoogle ResearchVerified email at google.com
Wenda ZhouNew York UniversityVerified email at nyu.edu
Zhen LinUniversity of Illinois at Urbana-ChampaignVerified email at illinois.edu
Vikas GargMassachusetts Institute of Technology (MIT)Verified email at mit.edu
Jean-Philippe VertOwkin / PSL UniversityVerified email at owkin.com

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	2738	2010
On representing chemical environments AP Bartók, R Kondor, G Csányi Physical Review B—Condensed Matter and Materials Physics 87 (18), 184115, 2013	2392	2013
Diffusion kernels on graphs and other discrete structures RI Kondor, J Lafferty Proceedings of the 19th international conference on machine learning 2002 …, 2002	1707	2002
Graph kernels SVN Vishwanathan, NN Schraudolph, R Kondor, KM Borgwardt The Journal of Machine Learning Research 11, 1201-1242, 2010	1378	2010
Kernels and regularization on graphs AJ Smola, R Kondor Learning Theory and Kernel Machines: 16th Annual Conference on Learning …, 2003	1125	2003
Probability product kernels T Jebara, R Kondor, A Howard The Journal of Machine Learning Research 5, 819-844, 2004	680	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	524	2018
Cormorant: Covariant molecular neural networks B Anderson, TS Hy, R Kondor Advances in neural information processing systems 32, 2019	478	2019
A kernel between sets of vectors R Kondor, T Jebara Proceedings of the 20th international conference on machine learning (ICML …, 2003	361	2003
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	290	2018
The multiscale laplacian graph kernel R Kondor, H Pan Advances in neural information processing systems 29, 2016	212	2016
Solving the multi-way matching problem by permutation synchronization D Pachauri, R Kondor, V Singh Advances in neural information processing systems 26, 2013	207	2013
Bhattacharyya and expected likelihood kernels T Jebara, R Kondor Learning Theory and Kernel Machines: 16th Annual Conference on Learning …, 2003	196	2003
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	164	2020
Covariant compositional networks for learning graphs R Kondor, HT Son, H Pan, B Anderson, S Trivedi arXiv preprint arXiv:1801.02144, 2018	147	2018
Group theoretical methods in machine learning IR Kondor Columbia University, 2008	147	2008
Atom3d: Tasks on molecules in three dimensions RJL Townshend, M Vögele, P Suriana, A Derry, A Powers, Y Laloudakis, ... arXiv preprint arXiv:2012.04035, 2020	123	2020
N-body networks: a covariant hierarchical neural network architecture for learning atomic potentials R Kondor arXiv preprint arXiv:1803.01588, 2018	121	2018
Multi-object tracking with representations of the symmetric group R Kondor, A Howard, T Jebara Artificial intelligence and statistics, 211-218, 2007	103	2007
Predicting molecular properties with covariant compositional networks TS Hy, S Trivedi, H Pan, BM Anderson, R Kondor The Journal of chemical physics 148 (24), 2018	97	2018

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