Daniel Hsu

Cited by

	All	Since 2016
Citations	8574	5843
h-index	41	36
i10-index	65	61

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Co-authors

Sham M KakadeUniversity of WashingtonVerified email at cs.washington.edu
Tong ZhangHKUSTVerified email at tongzhang-ml.org
Anima AnandkumarCalifornia Institute of Technology and NVIDIAVerified email at caltech.edu
John LangfordMicrosoft Research New YorkVerified email at hunch.net
Mikhail (Misha) BelkinProfessor of Data Science, Halıcıoğlu Data Science Institute, UCSDVerified email at ucsd.edu
Matus TelgarskyUniversity of Illinois, Urbana-ChampaignVerified email at illinois.edu
Sanjoy DasguptaProfessor of Computer Science, University of California, San DiegoVerified email at eng.ucsd.edu
Roxana GeambasuAssistant Professor of Computer Science, Columbia UniversityVerified email at columbia.edu
Dean FosterAmazon, NYCVerified email at amazon.com
Kamalika ChaudhuriAssociate Professor of Computer Science, University of California, San DiegoVerified email at eng.ucsd.edu
Rong GeDuke UniversityVerified email at cs.duke.edu
Ji XuColumbia UniversityVerified email at columbia.edu
Alekh AgarwalMicrosoft ResearchVerified email at microsoft.com
Vaggelis AtlidakisPostdoctoral Researcher, Brown UniversityVerified email at brown.edu
Mathias LécuyerPost-Doctoral Researcher, Microsoft Research, New YorkVerified email at microsoft.com
Nikos KarampatziakisMicrosoftVerified email at microsoft.com
Yi-Kai LiuComputer Scientist, NISTVerified email at nist.gov
soumik mandalPhD student, Ohio State UniversityVerified email at buckeyemail.osu.edu
Zoltan HaimanColumbia UniversityVerified email at astro.columbia.edu
Michael CollinsProfessor of Computer Science, Columbia University; Google NYCVerified email at cs.columbia.edu

Daniel Hsu

Columbia University

Verified email at cs.columbia.edu - Homepage

Algorithmic statistics machine learning


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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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