Daniel Hsu

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Sham M KakadeHarvard UniversityVerified email at seas.harvard.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
Roxana GeambasuAssistant Professor of Computer Science, Columbia UniversityVerified email at columbia.edu
Sanjoy DasguptaProfessor of Computer Science, University of California, San DiegoVerified email at eng.ucsd.edu
Ji XuColumbia UniversityVerified email at columbia.edu
Dean FosterAmazon, NYCVerified email at amazon.com
Kamalika ChaudhuriProfessor of Computer Science, UCSD. Research Scientist, Meta AIVerified email at eng.ucsd.edu
Rong GeDuke UniversityVerified email at cs.duke.edu
Vaggelis AtlidakisPostdoctoral Researcher, Brown UniversityVerified email at brown.edu
Mathias LécuyerUniversity of British ColumbiaVerified email at ubc.ca
Alekh AgarwalGoogleVerified email at google.com
Luis GravanoProfessor of Computer Science, Columbia UniversityVerified email at cs.columbia.edu
Suman JanaAssociate Professor of Computer Science, Columbia UniversityVerified email at cs.columbia.edu
Zoltan HaimanColumbia UniversityVerified email at astro.columbia.edu
Christopher ToshMemorial Sloan Kettering Cancer CenterVerified email at mskcc.org
Nikos KarampatziakisMicrosoftVerified email at microsoft.com

Daniel Hsu

Columbia University

Verified email at cs.columbia.edu - Homepage

Algorithmic statistics machine learning


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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	1229	2019
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	1191	2014
Certified robustness to adversarial examples with differential privacy M Lecuyer, V Atlidakis, R Geambasu, D Hsu, S Jana arXiv preprint arXiv:1802.03471, 2018	710	2018
A spectral algorithm for learning hidden markov models D Hsu, SM Kakade, T Zhang Arxiv preprint arXiv:0811.4413, 2008	559*	2008
Hierarchical sampling for active learning S Dasgupta, D Hsu Proceedings of the 25th international conference on Machine learning, 208-215, 2008	551	2008
Multi-label prediction via compressed sensing DJ Hsu, SM Kakade, J Langford, T Zhang Advances in neural information processing systems 22, 2009	508	2009
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	445	2014
A tail inequality for quadratic forms of subgaussian random vectors D Hsu, SM Kakade, T Zhang Arxiv preprint arXiv:1110.2842, 2011	412	2011
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	387*	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	362	2013
A general agnostic active learning algorithm S Dasgupta, D Hsu, C Monteleoni Advances in neural information processing systems 20, 353-360, 2007	357	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	354	2012
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	323	2004
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	297	2011
Two models of double descent for weak features M Belkin, D Hsu, J Xu SIAM Journal on Mathematics of Data Science 2 (4), 1167-1180, 2020	289	2020
A tensor spectral approach to learning mixed membership community models A Anandkumar, R Ge, D Hsu, S Kakade Conference on Learning Theory, 867-881, 2013	272	2013
Robust Matrix Decomposition with Sparse Corruptions D Hsu, SM Kakade, T Zhang Information Theory, IEEE Transactions on, 1-1, 2011	263	2011
Overfitting or perfect fitting? risk bounds for classification and regression rules that interpolate M Belkin, DJ Hsu, P Mitra Advances in neural information processing systems 31, 2018	251	2018
Random design analysis of ridge regression D Hsu, SM Kakade, T Zhang Conference on learning theory, 9.1-9.24, 2012	239	2012
Agnostic active learning without constraints A Beygelzimer, D Hsu, J Langford, T Zhang Arxiv preprint arXiv:1006.2588, 2010	200	2010

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