Mladen Kolar

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Eric XingPresident at Mohamed bin Zayed University of AI, Professor of Computer Science, Carnegie Mellon UVerified email at cs.cmu.edu
Han LiuAssociate Professor, Department of Computer Science, Northwestern UniversityVerified email at princeton.edu
Le SongGeorgia Institute of TechnologyVerified email at cc.gatech.edu
Jialei WangUniversity of ChicagoVerified email at uchicago.edu
Ming YuThe University of Chicago Booth School of BusinessVerified email at chicagobooth.edu
Zhaoran WangAssistant Professor at Northwestern UniversityVerified email at northwestern.edu
Varun GuptaAssociate Professor, Booth School of Business, University of ChicagoVerified email at uchicago.edu
Zhuoran YangPrinceton UniversityVerified email at princeton.edu
Alessandro RinaldoCarnegie Mellon UniversityVerified email at cmu.edu
Nathan SrebroProfessor, TTIC and University of ChicagoVerified email at ttic.edu
Aarti SinghAssociate Professor of Machine Learning Department, Carnegie Mellon UniversityVerified email at cmu.edu
Sivaraman BalakrishnanCarnegie Mellon UniversityVerified email at stat.cmu.edu
Larry WassermanProfessor, Department of Statistics and Machine Learning Department, Carnegie Mellon UniversityVerified email at stat.cmu.edu
Junwei LuAssistant Professor of Biostatistics, Harvard T.H. Chan School of Public HealthVerified email at hsph.harvard.edu
Amr AhmedSenior Staff Research Scientist, Google AIVerified email at google.com
Boxin ZhaoUniversity of ChicagoVerified email at uchicago.edu
Rina Foygel BarberUniversity of ChicagoVerified email at uchicago.edu
Jan SnajderAssoc. Prof, University of Zagreb, FER, CroatiaVerified email at fer.hr
Bojana Dalbelo BasicUniversity of Zagreb Faculty of Electrical Engineering and ComputingVerified email at fer.hr
Sanmi KoyejoAssistant Professor, University of Illinois at Urbana-ChampaignVerified email at illinois.edu

Mladen Kolar

The University of Chicago, Booth School of Business

Verified email at chicagobooth.edu - Homepage

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Estimating time-varying networks M Kolar, L Song, A Ahmed, EP Xing The Annals of Applied Statistics, 94-123, 2010	323	2010
Time-varying dynamic bayesian networks L Song, M Kolar, E Xing Advances in neural information processing systems 22, 2009	227	2009
KELLER: estimating time-varying interactions between genes L Song, M Kolar, EP Xing Bioinformatics 25 (12), i128-i136, 2009	127	2009
Efficient distributed learning with sparsity J Wang, M Kolar, N Srebro, T Zhang International Conference on Machine Learning, 3636-3645, 2017	96	2017
Sparsistent learning of varying-coefficient models with structural changes M Kolar, L Song, E Xing Advances in neural information processing systems 22, 2009	71	2009
On time varying undirected graphs M Kolar, E Xing Proceedings of the Fourteenth International Conference on Artificial …, 2011	66	2011
Minimax localization of structural information in large noisy matrices M Kolar, S Balakrishnan, A Rinaldo, A Singh Advances in Neural Information Processing Systems 24, 2011	66	2011
Distributed multi-task learning J Wang, M Kolar, N Srerbo Artificial intelligence and statistics, 751-760, 2016	62	2016
Estimating networks with jumps M Kolar, EP Xing Electronic journal of statistics 6, 2069, 2012	61	2012
Convergent policy optimization for safe reinforcement learning M Yu, Z Yang, M Kolar, Z Wang Advances in Neural Information Processing Systems 32, 2019	52	2019
Estimating sparse precision matrices from data with missing values M Kolar, EP Xing International Conference on Machine Learning, 635-642, 2012	52*	2012
Comparison of collocation extraction measures for document indexing S Petrovic, J Snajder, B Dalbelo-Basic, M Kolar 28th International Conference on Information Technology Interfaces, 2006 …, 2006	44	2006
Rocket: Robust confidence intervals via kendall’s tau for transelliptical graphical models RF Barber, M Kolar The Annals of Statistics 46 (6B), 3422-3450, 2018	42	2018
Sketching meets random projection in the dual: A provable recovery algorithm for big and high-dimensional data J Wang, J Lee, M Mahdavi, M Kolar, N Srebro Artificial Intelligence and Statistics, 1150-1158, 2017	42	2017
Feature selection in high-dimensional classification M Kolar, H Liu International Conference on Machine Learning, 329-337, 2013	42*	2013
Statistical and computational tradeoffs in biclustering S Balakrishnan, M Kolar, A Rinaldo, A Singh, L Wasserman NIPS 2011 workshop on computational trade-offs in statistical learning 4, 2011	36	2011
Union Support Recovery in Multi-task Learning. M Kolar, J Lafferty, L Wasserman Journal of Machine Learning Research 12 (7), 2011	35	2011
On sparse nonparametric conditional covariance selection M Kolar, A P Parikh, E P Xing Carnegie Mellon University, 2010	35	2010
Graph estimation from multi-attribute data M Kolar, H Liu, EP Xing Journal of Machine Learning Research, 2014	34	2014
Post-regularization inference for time-varying nonparanormal graphical models J Lu, M Kolar, H Liu Journal of Machine Learning Research, 2018	32*	2018

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