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A Rademacher Complexity Based Method fo rControlling Power and Confidence Level in Adaptive Statistical Analysis

Oct 04, 2019
Lorenzo De Stefani, Eli Upfal


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Uniform Convergence Bounds for Codec Selection

Dec 18, 2018
Clayton Sanford, Cyrus Cousins, Eli Upfal


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Unknown Examples & Machine Learning Model Generalization

Aug 24, 2018
Yeounoh Chung, Peter J. Haas, Eli Upfal, Tim Kraska


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Machine Learning in High Energy Physics Community White Paper

Jul 08, 2018
Kim Albertsson, Piero Altoe, Dustin Anderson, Michael Andrews, Juan Pedro Araque Espinosa, Adam Aurisano, Laurent Basara, Adrian Bevan, Wahid Bhimji, Daniele Bonacorsi, Paolo Calafiura, Mario Campanelli, Louis Capps, Federico Carminati, Stefano Carrazza, Taylor Childers, Elias Coniavitis, Kyle Cranmer, Claire David, Douglas Davis, Javier Duarte, Martin Erdmann, Jonas Eschle, Amir Farbin, Matthew Feickert, Nuno Filipe Castro, Conor Fitzpatrick, Michele Floris, Alessandra Forti, Jordi Garra-Tico, Jochen Gemmler, Maria Girone, Paul Glaysher, Sergei Gleyzer, Vladimir Gligorov, Tobias Golling, Jonas Graw, Lindsey Gray, Dick Greenwood, Thomas Hacker, John Harvey, Benedikt Hegner, Lukas Heinrich, Ben Hooberman, Johannes Junggeburth, Michael Kagan, Meghan Kane, Konstantin Kanishchev, Przemysław Karpiński, Zahari Kassabov, Gaurav Kaul, Dorian Kcira, Thomas Keck, Alexei Klimentov, Jim Kowalkowski, Luke Kreczko, Alexander Kurepin, Rob Kutschke, Valentin Kuznetsov, Nicolas Köhler, Igor Lakomov, Kevin Lannon, Mario Lassnig, Antonio Limosani, Gilles Louppe, Aashrita Mangu, Pere Mato, Narain Meenakshi, Helge Meinhard, Dario Menasce, Lorenzo Moneta, Seth Moortgat, Mark Neubauer, Harvey Newman, Hans Pabst, Michela Paganini, Manfred Paulini, Gabriel Perdue, Uzziel Perez, Attilio Picazio, Jim Pivarski, Harrison Prosper, Fernanda Psihas, Alexander Radovic, Ryan Reece, Aurelius Rinkevicius, Eduardo Rodrigues, Jamal Rorie, David Rousseau, Aaron Sauers, Steven Schramm, Ariel Schwartzman, Horst Severini, Paul Seyfert, Filip Siroky, Konstantin Skazytkin, Mike Sokoloff, Graeme Stewart, Bob Stienen, Ian Stockdale, Giles Strong, Savannah Thais, Karen Tomko, Eli Upfal, Emanuele Usai, Andrey Ustyuzhanin, Martin Vala, Sofia Vallecorsa, Mauro Verzetti, Xavier Vilasís-Cardona, Jean-Roch Vlimant, Ilija Vukotic, Sean-Jiun Wang, Gordon Watts, Michael Williams, Wenjing Wu, Stefan Wunsch, Omar Zapata

* Editors: Sergei Gleyzer, Paul Seyfert and Steven Schramm 

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Bandits and Experts in Metric Spaces

Apr 27, 2018
Robert Kleinberg, Aleksandrs Slivkins, Eli Upfal

* This manuscript is a merged and definitive version of (R. Kleinberg, Slivkins, Upfal: STOC 2008) and (R. Kleinberg, Slivkins: SODA 2010), with a significantly revised presentation 

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Optimizing Static and Adaptive Probing Schedules for Rapid Event Detection

Sep 10, 2015
Ahmad Mahmoody, Evgenios M. Kornaropoulos, Eli Upfal


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Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees

Feb 22, 2013
Matteo Riondato, Eli Upfal

* 19 pages, 7 figures. A shorter version of this paper appeared in the proceedings of ECML PKDD 2012 

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A Clustering Approach to Solving Large Stochastic Matching Problems

Jan 10, 2013
Milos Hauskrecht, Eli Upfal

* Appears in Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI2001) 

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The VC-Dimension of Queries and Selectivity Estimation Through Sampling

Aug 11, 2011
Matteo Riondato, Mert Akdere, Ugur Cetintemel, Stanley B. Zdonik, Eli Upfal

* 20 pages, 3 figures 

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Multi-Armed Bandits in Metric Spaces

Sep 29, 2008
Robert Kleinberg, Aleksandrs Slivkins, Eli Upfal

* 16 pages, 0 figures 

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