Daniel Hsu

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Citations	8574	5843
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Coauteurs

Sham M KakadeUniversity of WashingtonAdresse e-mail validée de cs.washington.edu
Tong ZhangHKUSTAdresse e-mail validée de tongzhang-ml.org
Anima AnandkumarCalifornia Institute of Technology and NVIDIAAdresse e-mail validée de caltech.edu
John LangfordMicrosoft Research New YorkAdresse e-mail validée de hunch.net
Mikhail (Misha) BelkinProfessor of Data Science, Halıcıoğlu Data Science Institute, UCSDAdresse e-mail validée de ucsd.edu
Matus TelgarskyUniversity of Illinois, Urbana-ChampaignAdresse e-mail validée de illinois.edu
Sanjoy DasguptaProfessor of Computer Science, University of California, San DiegoAdresse e-mail validée de eng.ucsd.edu
Roxana GeambasuAssistant Professor of Computer Science, Columbia UniversityAdresse e-mail validée de columbia.edu
Dean FosterAmazon, NYCAdresse e-mail validée de amazon.com
Kamalika ChaudhuriAssociate Professor of Computer Science, University of California, San DiegoAdresse e-mail validée de eng.ucsd.edu
Rong GeDuke UniversityAdresse e-mail validée de cs.duke.edu
Ji XuColumbia UniversityAdresse e-mail validée de columbia.edu
Alekh AgarwalMicrosoft ResearchAdresse e-mail validée de microsoft.com
Vaggelis AtlidakisPostdoctoral Researcher, Brown UniversityAdresse e-mail validée de brown.edu
Mathias LécuyerPost-Doctoral Researcher, Microsoft Research, New YorkAdresse e-mail validée de microsoft.com
Nikos KarampatziakisMicrosoftAdresse e-mail validée de microsoft.com
Yi-Kai LiuComputer Scientist, NISTAdresse e-mail validée de nist.gov
soumik mandalPhD student, Ohio State UniversityAdresse e-mail validée de buckeyemail.osu.edu
Zoltan HaimanColumbia UniversityAdresse e-mail validée de astro.columbia.edu
Michael CollinsProfessor of Computer Science, Columbia University; Google NYCAdresse e-mail validée de cs.columbia.edu

Daniel Hsu

Columbia University

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

Algorithmic statistics machine learning


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Tensor decompositions for learning latent variable models A Anandkumar, R Ge, D Hsu, SM Kakade, M Telgarsky Journal of Machine Learning Research 15, 2773-2832, 2014	929	2014
A spectral algorithm for learning hidden markov models D Hsu, SM Kakade, T Zhang Arxiv preprint arXiv:0811.4413, 2008	504*	2008
Multi-label prediction via compressed sensing DJ Hsu, SM Kakade, J Langford, T Zhang Advances in neural information processing systems 22, 772-780, 2009	454	2009
Hierarchical sampling for active learning S Dasgupta, D Hsu Proceedings of the 25th international conference on Machine learning, 208-215, 2008	442	2008
A spectral algorithm for latent dirichlet allocation A Anandkumar, Y Liu, D Hsu, DP Foster, SM Kakade Advances in Neural Information Processing Systems, 917-925, 2012	357*	2012
Reconciling modern machine-learning practice and the classical bias–variance trade-off M Belkin, D Hsu, S Ma, S Mandal Proceedings of the National Academy of Sciences 116 (32), 15849-15854, 2019	349*	2019
A general agnostic active learning algorithm S Dasgupta, D Hsu, C Monteleoni Advances in neural information processing systems 20, 353-360, 2007	314	2007
A method of moments for mixture models and hidden Markov models A Anandkumar, D Hsu, SM Kakade Conference on Learning Theory, 33.1-33.34, 2012	312	2012
Learning mixtures of spherical gaussians: moment methods and spectral decompositions D Hsu, SM Kakade Proceedings of the 4th conference on Innovations in Theoretical Computer …, 2013	308	2013
a CAPpella: programming by demonstration of context-aware applications AK Dey, R Hamid, C Beckmann, I Li, D Hsu Proceedings of the SIGCHI conference on Human factors in computing systems …, 2004	306	2004
Taming the monster: A fast and simple algorithm for contextual bandits A Agarwal, D Hsu, S Kale, J Langford, L Li, RE Schapire Thirty-First International Conference on Machine Learning, 2014	296	2014
A tail inequality for quadratic forms of subgaussian random vectors D Hsu, SM Kakade, T Zhang Arxiv preprint arXiv:1110.2842, 2011	293	2011
Certified robustness to adversarial examples with differential privacy M Lecuyer, V Atlidakis, R Geambasu, D Hsu, S Jana arXiv preprint arXiv:1802.03471, 2018	264	2018
A tensor approach to learning mixed membership community models A Anandkumar, R Ge, D Hsu, SM Kakade The Journal of Machine Learning Research 15 (1), 2239-2312, 2014	243	2014
Robust Matrix Decomposition with Sparse Corruptions D Hsu, SM Kakade, T Zhang Information Theory, IEEE Transactions on, 1-1, 2011	235	2011
Efficient optimal learning for contextual bandits M Dudik, D Hsu, S Kale, N Karampatziakis, J Langford, L Reyzin, T Zhang arXiv preprint arXiv:1106.2369, 2011	217	2011
Agnostic active learning without constraints A Beygelzimer, D Hsu, J Langford, T Zhang Arxiv preprint arXiv:1006.2588, 2010	183	2010
Random design analysis of ridge regression D Hsu, SM Kakade, T Zhang Conference on learning theory, 9.1-9.24, 2012	161	2012
Stochastic convex optimization with bandit feedback A Agarwal, DP Foster, D Hsu, SM Kakade, A Rakhlin Advances in Neural Information Processing Systems, 1035-1043, 2011	126	2011
Loss minimization and parameter estimation with heavy tails D Hsu, S Sabato The Journal of Machine Learning Research 17 (1), 543-582, 2016	123	2016

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