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

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Ambuj Tewari | अम्बुज तिवारी

Professor, Statistics and EECS, University of Michigan

Email yang diverifikasi di umich.edu - Beranda

Machine Learning Statistics Data Science Sequential Decision Making Optimization


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
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	1878*	2018
Learning with noisy labels N Natarajan, IS Dhillon, PK Ravikumar, A Tewari Advances in neural information processing systems 26, 2013	1412	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	640	2015
Stochastic methods for l₁ regularized loss minimization S Shalev-Shwartz, A Tewari Proceedings of the 26th Annual International Conference on Machine Learning …, 2009	520	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	438	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	436	2008
Composite objective mirror descent. JC Duchi, S Shalev-Shwartz, Y Singer, A Tewari Colt 10, 14-26, 2010	422	2010
On the Consistency of Multiclass Classification Methods. A Tewari, PL Bartlett Journal of Machine Learning Research 8 (5), 2007	365	2007
Smoothness, low noise and fast rates N Srebro, K Sridharan, A Tewari Advances in neural information processing systems 23, 2010	314	2010
REGAL: A regularization based algorithm for reinforcement learning in weakly communicating MDPs PL Bartlett, A Tewari arXiv preprint arXiv:1205.2661, 2012	311	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	262	2014
From ads to interventions: Contextual bandits in mobile health A Tewari, SA Murphy Mobile health: sensors, analytic methods, and applications, 495-517, 2017	238	2017
Mixture proportion estimation via kernel embeddings of distributions H Ramaswamy, C Scott, A Tewari International conference on machine learning, 2052-2060, 2016	229	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	223	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	219	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	215	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	203	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
On the generalization ability of online strongly convex programming algorithms SM Kakade, A Tewari Advances in neural information processing systems 21, 2008	202	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	182	2014

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