Rocco Anthony Servedio

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Ryan O'DonnellProfessor, Computer Science Department, Carnegie MellonAdresse e-mail validée de cs.cmu.edu
Ilias DiakonikolasUniversity of Wisconsin-MadisonAdresse e-mail validée de wisc.edu
Li-Yang TanStanford UniversityAdresse e-mail validée de cs.stanford.edu
Adam KlivansProfessor of Computer Science, University of Texas at AustinAdresse e-mail validée de cs.utexas.edu
Xi ChenColumbia UniversityAdresse e-mail validée de cs.columbia.edu
Anindya DeUniversity of PennsylvaniaAdresse e-mail validée de seas.upenn.edu
Andrew WanSnapAdresse e-mail validée de cs.columbia.edu
Jon FeldmanResearcher, GoogleAdresse e-mail validée de google.com
Homin K. LeeDatadogAdresse e-mail validée de columbia.edu
Parikshit GopalanAppleAdresse e-mail validée de apple.com
Tal MalkinProfessor of Computer Science, Columbia UniversityAdresse e-mail validée de columbia.edu
Constantinos DaskalakisProfessor of Computer Science, MITAdresse e-mail validée de csail.mit.edu
Emanuele ViolaProfessor of Computer Science, Northeastern UniversityAdresse e-mail validée de ccs.neu.edu
Jeffrey C. JacksonProfessor of Computer Science, Duquesne UniversityAdresse e-mail validée de duq.edu
Kevin MatulefMIT, Tsinghua, AarhusAdresse e-mail validée de mit.edu
Ronitt RubinfeldProfessor of Computer Science, MIT and Tel Aviv UniversityAdresse e-mail validée de csail.mit.edu
Shivam NadimpalliMassachusetts Institute of TechnologyAdresse e-mail validée de mit.edu
Elchanan MosselProfessor of Mathematics, MITAdresse e-mail validée de mit.edu
Roni KhardonProfessor of Computer Science, Indiana University, BloomingtonAdresse e-mail validée de iu.edu
Clément L. CanonneUniversity of SydneyAdresse e-mail validée de sydney.edu.au

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Rocco Anthony Servedio

Department of Computer Science, Columbia University

Adresse e-mail validée de columbia.edu - Page d'accueil

Theoretical computer science computational learning theory computational complexity theory machine learning


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Random classification noise defeats all convex potential boosters PM Long, RA Servedio Proceedings of the 25th international conference on Machine learning, 608-615, 2008	364	2008
Agnostically learning halfspaces AT Kalai, AR Klivans, Y Mansour, RA Servedio SIAM Journal on Computing 37 (6), 1777-1805, 2008	363	2008
Learning functions of k relevant variables E Mossel, R O'Donnell, RA Servedio Journal of Computer and System Sciences 69 (3), 421-434, 2004	295*	2004
Learning DNF in time AR Klivans, R Servedio Proceedings of the thirty-third annual ACM symposium on Theory of computing …, 2001	259	2001
Smooth boosting and learning with malicious noise RA Servedio The Journal of Machine Learning Research 4, 633-648, 2003	248	2003
On the capacity of secure network coding J Feldman, T Malkin, C Stein, RA Servedio Proc. 42nd Annual Allerton Conference on Communication, Control, and …, 2004	234	2004
Learning intersections and thresholds of halfspaces AR Klivans, R O'Donnell, RA Servedio Journal of Computer and System Sciences 68 (4), 808-840, 2004	232	2004
Bounded independence fools halfspaces I Diakonikolas, P Gopalan, R Jaiswal, RA Servedio, E Viola SIAM Journal on Computing 39 (8), 3441-3462, 2010	164	2010
Every decision tree has an influential variable R O'Donnell, M Saks, O Schramm, RA Servedio 46th annual IEEE symposium on foundations of computer science (FOCS'05), 31-39, 2005	163	2005
Equivalences and separations between quantum and classical learnability RA Servedio, SJ Gortler SIAM Journal on Computing 33 (5), 1067-1092, 2004	147	2004
Learning poisson binomial distributions C Daskalakis, I Diakonikolas, RA Servedio Proceedings of the forty-fourth annual ACM symposium on Theory of computing …, 2012	145	2012
Learning monotone decision trees in polynomial time R O'Donnell, RA Servedio SIAM Journal on Computing 37 (3), 827-844, 2007	139	2007
Efficient density estimation via piecewise polynomial approximation SO Chan, I Diakonikolas, RA Servedio, X Sun Proceedings of the forty-sixth annual ACM symposium on Theory of computing …, 2014	135	2014
Learning geometric concepts via Gaussian surface area AR Klivans, R O'Donnell, RA Servedio 2008 49th Annual IEEE Symposium on Foundations of Computer Science, 541-550, 2008	134	2008
Learning mixtures of product distributions over discrete domains J Feldman, R O'Donnell, RA Servedio SIAM Journal on Computing 37 (5), 1536-1564, 2008	133	2008
Learning Halfspaces with Malicious Noise. AR Klivans, PM Long, RA Servedio Journal of Machine Learning Research 10 (12), 2009	131	2009
LP decoding corrects a constant fraction of errors J Feldman, T Malkin, RA Servedio, C Stein, MJ Wainwright IEEE Transactions on Information Theory 53 (1), 82-89, 2006	130	2006
Testing halfspaces K Matulef, R O'Donnell, R Rubinfeld, RA Servedio SIAM Journal on Computing 39 (5), 2004-2047, 2010	124	2010
Testing for concise representations I Diakonikolas, HK Lee, K Matulef, K Onak, R Rubinfeld, RA Servedio, ... 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07), 549-558, 2007	122	2007
Testing Fourier dimensionality and sparsity P Gopalan, R O'Donnell, RA Servedio, A Shpilka, K Wimmer SIAM Journal on Computing 40 (4), 1075-1100, 2011	120	2011

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