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, Computer Scinece and Engineering, 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
Kamalika ChaudhuriProfessor of Computer Science, UCSD. Research Scientist, Meta AIVerified email at ucsd.edu
Vaggelis AtlidakisLaceworkVerified email at brown.edu
Rong GeDuke UniversityVerified email at cs.duke.edu
Mathias LécuyerUniversity of British ColumbiaVerified email at ubc.ca
Dean FosterAmazon, NYCVerified email at amazon.com
Alekh AgarwalGoogleVerified email at google.com
Christopher ToshMemorial Sloan Kettering Cancer CenterVerified email at mskcc.org
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
soumik mandalPhD student, Ohio State UniversityVerified email at buckeyemail.osu.edu
Zoltan HaimanColumbia UniversityVerified email at astro.columbia.edu

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	1577	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	1278	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	853	2018
A spectral algorithm for learning hidden markov models D Hsu, SM Kakade, T Zhang Arxiv preprint arXiv:0811.4413, 2008	587*	2008
Hierarchical sampling for active learning S Dasgupta, D Hsu Proceedings of the 25th international conference on Machine learning, 208-215, 2008	580	2008
Multi-label prediction via compressed sensing DJ Hsu, SM Kakade, J Langford, T Zhang Advances in neural information processing systems 22, 2009	519	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	504	2014
A tail inequality for quadratic forms of subgaussian random vectors D Hsu, SM Kakade, T Zhang Arxiv preprint arXiv:1110.2842, 2011	436	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	389	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	379	2013
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	375	2012
A general agnostic active learning algorithm S Dasgupta, D Hsu, C Monteleoni Advances in neural information processing systems 20, 353-360, 2007	359	2007
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	345	2020
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	331	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	323	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	292	2018
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	287	2013
Robust Matrix Decomposition with Sparse Corruptions D Hsu, SM Kakade, T Zhang Information Theory, IEEE Transactions on, 1-1, 2011	269	2011
Random design analysis of ridge regression D Hsu, SM Kakade, T Zhang Conference on learning theory, 9.1-9.24, 2012	258	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	206	2011

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