Andrew R Barron

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Bin YUProfessor of Statistics and EECS, UC BerkeleyVerified email at stat.berkeley.edu
Yuhong YangProfessor of Statistics, University of MinnesotaVerified email at umn.edu
Mokshay MadimanUniversity of DelawareVerified email at udel.edu
Lidija JakobekFull Professor, J. J. Strossmayer University of OsijekVerified email at ptfos.hr
Gilbert LeungUber Technologies, Inc.Verified email at alum.mit.edu
Larry WassermanProfessor, Department of Statistics and Machine Learning Department, Carnegie Mellon UniversityVerified email at stat.cmu.edu
Xi (Rossi) LUOUniversity of Texas Health Science Center at HoustonVerified email at uth.tmc.edu
Albert Cohenprofesseur de mathématiques, Université Paris VIVerified email at ann.jussieu.fr
Ronald DeVoreProfessor of Matematics, Texas A&M UniversityVerified email at math.tamu.edu
Wolfgang DahmenProfessor for Mathematics, University of South CarolinaVerified email at math.sc.edu
László GyörfiProfessor of Information Theory, Budapest University of Technology and EconomicsVerified email at cs.bme.hu
David HausslerScientific Director, UC Santa Cruz Genomics Institute, University of California, Santa CruzVerified email at soe.ucsc.edu
Gerald H L CheangSenior Lecturer, School of Information Technology and Mathematical Sciences, University of SouthVerified email at unisa.edu.au
Petra MatićFaculty of Food Technology OsijekVerified email at ptfos.hr
Mirta BensicProfessor at the Department of Mathematics, University of OsijekVerified email at mathos.hr
Nicolas W HengartnerLos Alamos National LaboratoryVerified email at lanl.gov
Kazuho WatanabeToyohashi University of TechnologyVerified email at cs.tut.ac.jp
Teemu RoosProfessor at University of HelsinkiVerified email at helsinki.fi
Kristian SaboFull Professor Applied Mathematics, University OsijekVerified email at mathos.hr

Andrew R Barron

Professor of Statistics, Yale University

Verified email at yale.edu - Homepage

Statistics Probability Information Theory Machine Learning Neural Networks


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Universal approximation bounds for superpositions of a sigmoidal function AR Barron IEEE Transactions on Information theory 39 (3), 930-945, 1993	3445	1993
The minimum description length principle in coding and modeling A Barron, J Rissanen, B Yu IEEE transactions on information theory 44 (6), 2743-2760, 1998	1382	1998
Approximation and estimation bounds for artificial neural networks AR Barron Machine learning 14, 115-133, 1994	960	1994
Risk bounds for model selection via penalization A Barron, L Birgé, P Massart Probability theory and related fields 113, 301-413, 1999	920	1999
Minimum complexity density estimation AR Barron, TM Cover IEEE transactions on information theory 37 (4), 1034-1054, 1991	691	1991
Information-theoretic determination of minimax rates of convergence Y Yang, A Barron Annals of Statistics, 1564-1599, 1999	576	1999
Information-theoretic asymptotics of Bayes methods BS Clarke, AR Barron IEEE Transactions on information theory 36 (3), 453-471, 1990	570	1990
The consistency of posterior distributions in nonparametric problems A Barron, MJ Schervish, L Wasserman The Annals of Statistics 27 (2), 536-561, 1999	462	1999
Entropy and the central limit theorem AR Barron The Annals of probability, 336-342, 1986	449	1986
Jeffreys' prior is asymptotically least favorable under entropy risk BS Clarke, AR Barron Journal of Statistical planning and Inference 41 (1), 37-60, 1994	396	1994
Predicted squared error: a criterion for automatic model selection AR Barron Self-organizing methods in modeling, 87-103, 2020	382	2020
Mixture density estimation J Li, A Barron Advances in neural information processing systems 12, 1999	363	1999
Approximation and learning by greedy algorithms AR Barron, A Cohen, W Dahmen, RA DeVore	351	2008
Approximation of density functions by sequences of exponential families AR Barron, CH Sheu The Annals of Statistics 19 (3), 1347-1369, 1991	342	1991
Statistical learning networks: A unifying view AR Barron, RL Barron Symposium on the interface: Statistics and computing science. April, 21-23, 1988	314	1988
Information theory and mixing least-squares regressions G Leung, AR Barron IEEE Transactions on information theory 52 (8), 3396-3410, 2006	300	2006
The strong ergodic theorem for densities: generalized Shannon-McMillan-Breiman theorem AR Barron The annals of Probability 13 (4), 1292-1303, 1985	295	1985
Complexity regularization with application to artificial neural networks AR Barron Nonparametric functional estimation and related topics, 561-576, 1991	286	1991
Neural net approximation AR Barron Proc. 7th Yale workshop on adaptive and learning systems 1, 69-72, 1992	269	1992
Asymptotic minimax regret for data compression, gambling, and prediction Q Xie, AR Barron IEEE Transactions on Information Theory 46 (2), 431-445, 2000	245	2000

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