David Pal - Google Scholar

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	All	Since 2015
Citations	2242	1575
h-index	18	16
i10-index	22	21

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Csaba SzepesvariDeepMind & Department of Computing Science, University of AlbertaVerified email at cs.ualberta.ca
Shai Ben-DavidUniverity of Waterloo, CanadaVerified email at uwaterloo.ca
Tyler LuResearch Scientist, GoogleVerified email at cs.toronto.edu
Martin PálGoogle, Inc.Verified email at google.com
Yasin Abbasi-YadkoriDeepMindVerified email at google.com
Francesco OrabonaAssistant Professor, Boston UniversityVerified email at orabona.com
Gábor BartókETH ZurichVerified email at ualberta.ca
Barnabas PoczosAssociate professor, Carnegie Mellon UniversityVerified email at cs.cmu.edu
S MuthukrishnanRutgers UnivVerified email at cs.rutgers.edu
Ulrike von LuxburgUniversity of TübingenVerified email at informatik.uni-tuebingen.de
Shai Shalev-ShwartzThe Hebrew UniversityVerified email at cs.huji.ac.il
Gagan AggarwalResearch Scientist, GoogleVerified email at google.com
Hans Ulrich SimonProfessor of Computer Science, Ruhr-University BochumVerified email at rub.de
Alexander RakhlinAssociate Professor, MITVerified email at mit.edu
Satyen KaleStaff Research Scientist, Google ResearchVerified email at google.com
Balazs Szorenyiresearch fellow at the Research Group on Artificial Intelligence of the Hungarian Academy ofVerified email at inf.u-szeged.hu
Zohar KarninamazonVerified email at amazon.com
András AntosBudapest University of Technology and EconomicsVerified email at cs.bme.hu
Tengyuan LiangUniversity of Chicago, Booth School of BusinessVerified email at chicagobooth.edu
Chansoo LeeGoogleVerified email at google.com

David Pal

Principal Applied Researcher, Expedia Group

Verified email at expedia.com - Homepage

Machine Learning Algorithms Game Theory Combinatorics Probability Theory


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Improved algorithms for linear stochastic bandits Y Abbasi-Yadkori, D Pál, C Szepesvári Advances in Neural Information Processing Systems, 2312-2320, 2011	633	2011
A sober look at clustering stability S Ben-David, U Von Luxburg, D Pál International Conference on Computational Learning Theory, 5-19, 2006	253	2006
Contextual multi-armed bandits T Lu, D Pál, M Pál Proceedings of the Thirteenth international conference on Artificial …, 2010	199	2010
Estimation of Rényi entropy and mutual information based on generalized nearest-neighbor graphs D Pál, B Póczos, C Szepesvári Advances in Neural Information Processing Systems, 1849-1857, 2010	142	2010
Impossibility theorems for domain adaptation SB David, T Lu, T Luu, D Pál Proceedings of the Thirteenth International Conference on Artificial …, 2010	131	2010
Does Unlabeled Data Provably Help? Worst-case Analysis of the Sample Complexity of Semi-Supervised Learning. S Ben-David, T Lu, D Pál COLT, 33-44, 2008	122	2008
General auction mechanism for search advertising G Aggarwal, S Muthukrishnan, D Pál, M Pál Proceedings of the 18th international conference on World wide web, 241-250, 2009	115	2009
Agnostic Online Learning. S Ben-David, D Pál, S Shalev-Shwartz COLT 3, 1, 2009	93	2009
Stability of k-Means Clustering S Ben-David, D Pál, HU Simon International conference on computational learning theory, 20-34, 2007	89	2007
Online-to-confidence-set conversions and application to sparse stochastic bandits Y Abbasi-Yadkori, D Pal, C Szepesvari Artificial Intelligence and Statistics, 1-9, 2012	88	2012
Partial monitoring—classification, regret bounds, and algorithms G Bartók, DP Foster, D Pál, A Rakhlin, C Szepesvári Mathematics of Operations Research 39 (4), 967-997, 2014	84	2014
Coin betting and parameter-free online learning F Orabona, D Pál Advances in Neural Information Processing Systems, 577-585, 2016	48	2016
Minimax regret of finite partial-monitoring games in stochastic environments G Bartók, D Pál, C Szepesvári Proceedings of the 24th Annual Conference on Learning Theory, 133-154, 2011	43	2011
Online least squares estimation with self-normalized processes: An application to bandit problems Y Abbasi-Yadkori, D Pál, C Szepesvári arXiv preprint arXiv:1102.2670, 2011	30	2011
Toward a classification of finite partial-monitoring games A Antos, G Bartók, D Pál, C Szepesvári Theoretical Computer Science 473, 77-99, 2013	24	2013
Scale-free online learning F Orabona, D Pál Theoretical Computer Science 716, 50-69, 2018	22	2018
Toward a classification of finite partial-monitoring games G Bartók, D Pál, C Szepesvári International Conference on Algorithmic Learning Theory, 224-238, 2010	21	2010
Scale-free algorithms for online linear optimization F Orabona, D Pál International Conference on Algorithmic Learning Theory, 287-301, 2015	20	2015
Learning low density separators S Ben-David, T Lu, D Pál, M Sotáková Artificial Intelligence and Statistics, 25-32, 2009	16	2009
Adaptive feature selection: Computationally efficient online sparse linear regression under rip S Kale, Z Karnin, T Liang, D Pál arXiv preprint arXiv:1706.04690, 2017	13	2017

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