Daniel Hsu
Daniel Hsu
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Cited by
Cited by
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
Tensor decompositions for learning latent variable models.
A Anandkumar, R Ge, DJ Hsu, SM Kakade, M Telgarsky
J. Mach. Learn. Res. 15 (1), 2773-2832, 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
Hierarchical sampling for active learning
S Dasgupta, D Hsu
Proceedings of the 25th international conference on Machine learning, 208-215, 2008
A spectral algorithm for learning hidden markov models
D Hsu, SM Kakade, T Zhang
Arxiv preprint arXiv:0811.4413, 2008
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
Multi-label prediction via compressed sensing
DJ Hsu, SM Kakade, J Langford, T Zhang
Advances in neural information processing systems 22, 2009
A tail inequality for quadratic forms of subgaussian random vectors
D Hsu, SM Kakade, T Zhang
Arxiv preprint arXiv:1110.2842, 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
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
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
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
A general agnostic active learning algorithm
S Dasgupta, D Hsu, C Monteleoni
Advances in neural information processing systems 20, 353-360, 2007
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
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
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
Robust Matrix Decomposition with Sparse Corruptions
D Hsu, SM Kakade, T Zhang
Information Theory, IEEE Transactions on, 1-1, 2011
Discovering unwarranted associations in data-driven applications with the fairtest testing toolkit
F Tramèr, V Atlidakis, R Geambasu, DJ Hsu, JP Hubaux, M Humbert, ...
CoRR, abs/1510.02377, 2015
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
Agnostic active learning without constraints
A Beygelzimer, D Hsu, J Langford, T Zhang
Arxiv preprint arXiv:1006.2588, 2010
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