‪Ambuj Tewari | अम्बुज तिवारी‬

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Inderjit S. DhillonDistinguished Scientist at Google & Professor of Computer Science at UT Austin, Ex-VP at AmazonEmail verificata su cs.utexas.edu
Peter BartlettProfessor, EECS and Statistics, UC BerkeleyEmail verificata su cs.berkeley.edu
Karthik SridharanCornell University, University of Pennsylvania, Toyota Technological InstituteEmail verificata su ttic.edu
George MichailidisProfessor of Statistics and Computer ScienceEmail verificata su ufl.edu
Mohamad Kazem Shirani FaradonbehSouthern Methodist UniversityEmail verificata su smu.edu
Susan MurphyHarvard UniversityEmail verificata su g.harvard.edu
Nagarajan NatarajanResearcher, Microsoft Research IndiaEmail verificata su microsoft.com
Alexander RakhlinProfessor, MITEmail verificata su mit.edu
Sham M KakadeHarvard UniversityEmail verificata su seas.harvard.edu
Pradeep RavikumarProfessor, School of Computer Science, Carnegie Mellon UniversityEmail verificata su cs.cmu.edu
Shai Shalev-ShwartzThe Hebrew UniversityEmail verificata su cs.huji.ac.il
Paul ZimmermanUniversity of MichiganEmail verificata su umich.edu
Jacob AbernethyAssistant Professor, University of MichiganEmail verificata su umich.edu
Ziping XuPostdoc Fellow at Harvard UniversityEmail verificata su fas.harvard.edu
Yangyi LuPhD student of Statistics, University of MichiganEmail verificata su umich.edu
Jack GoetzMetaEmail verificata su umich.edu
Predrag KlasnjaAssociate Professor, School of Information, University of MichiganEmail verificata su umich.edu
Vinod RamanUniversity of MichiganEmail verificata su umich.edu
Prateek JainGoogle Research IndiaEmail verificata su google.com
Aditya ModiMicrosoftEmail verificata su umich.edu

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Ambuj Tewari | अम्बुज तिवारी

Professor, Statistics and EECS, University of Michigan

Email verificata su umich.edu - Home page

Machine Learning Statistics Data Science Sequential Decision Making Optimization


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
Just in time adaptive interventions (JITAIs): An organizing framework for ongoing health behavior support I Nahum-Shani, SN Smith, B Spring, LM Collins, K Witkiewitz, A Tewari, ... Annals of Behavioral Medicine 52 (6), 446-462, 2018	1951*	2018
Learning with noisy labels N Natarajan, IS Dhillon, PK Ravikumar, A Tewari Advances in neural information processing systems 26, 2013	1446	2013
Microrandomized trials: An experimental design for developing just-in-time adaptive interventions. P Klasnja, EB Hekler, S Shiffman, A Boruvka, D Almirall, A Tewari, ... Health Psychology 34 (S), 1220, 2015	660	2015
Stochastic methods for l₁ regularized loss minimization S Shalev-Shwartz, A Tewari Proceedings of the 26th Annual International Conference on Machine Learning …, 2009	527	2009
PAC subset selection in stochastic multi-armed bandits S Kalyanakrishnan, A Tewari, P Auer, P Stone Proceedings of the 29th International Conference on Machine Learning (ICML …, 2012	451	2012
On the complexity of linear prediction: Risk bounds, margin bounds, and regularization SM Kakade, K Sridharan, A Tewari Advances in neural information processing systems 21, 2008	442	2008
Composite objective mirror descent. JC Duchi, S Shalev-Shwartz, Y Singer, A Tewari Colt 10, 14-26, 2010	426	2010
On the Consistency of Multiclass Classification Methods. A Tewari, PL Bartlett Journal of Machine Learning Research 8 (5), 2007	370	2007
Smoothness, low noise and fast rates N Srebro, K Sridharan, A Tewari Advances in neural information processing systems 23, 2010	325	2010
REGAL: A regularization based algorithm for reinforcement learning in weakly communicating MDPs PL Bartlett, A Tewari arXiv preprint arXiv:1205.2661, 2012	314	2012
On iterative hard thresholding methods for high-dimensional M-estimation P Jain, A Tewari, P Kar Advances in Neural Information Processing Systems, 685-693, 2014	269	2014
From ads to interventions: Contextual bandits in mobile health A Tewari, SA Murphy Mobile health: sensors, analytic methods, and applications, 495-517, 2017	249	2017
Mixture proportion estimation via kernel embeddings of distributions H Ramaswamy, C Scott, A Tewari International conference on machine learning, 2052-2060, 2016	235	2016
Online bandit learning against an adaptive adversary: from regret to policy regret R Arora, O Dekel, A Tewari arXiv preprint arXiv:1206.6400, 2012	228	2012
Regularization techniques for learning with matrices SM Kakade, S Shalev-Shwartz, A Tewari The Journal of Machine Learning Research 13 (1), 1865-1890, 2012	221	2012
Exploiting longer cycles for link prediction in signed networks KY Chiang, N Natarajan, A Tewari, IS Dhillon Proceedings of the 20th ACM international conference on Information and …, 2011	217	2011
Optimal strategies and minimax lower bounds for online convex games J Abernethy, PL Bartlett, A Rakhlin, A Tewari Proceedings of the 21st annual conference on learning theory, 414-424, 2008	206	2008
On the generalization ability of online strongly convex programming algorithms SM Kakade, A Tewari Advances in neural information processing systems 21, 2008	204	2008
Efficient bandit algorithms for online multiclass prediction SM Kakade, S Shalev-Shwartz, A Tewari Proceedings of the 25th international conference on Machine learning, 440-447, 2008	203	2008
Prediction and clustering in signed networks: a local to global perspective KY Chiang, CJ Hsieh, N Natarajan, IS Dhillon, A Tewari The Journal of Machine Learning Research 15 (1), 1177-1213, 2014	184	2014

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