Daniel Hsu
Daniel Hsu
Verified email at cs.columbia.edu - Homepage
Cited by
Cited by
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
A spectral algorithm for learning hidden markov models
D Hsu, SM Kakade, T Zhang
Arxiv preprint arXiv:0811.4413, 2008
Multi-label prediction via compressed sensing
DJ Hsu, SM Kakade, J Langford, T Zhang
Advances in neural information processing systems, 772-780, 2009
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 latent dirichlet allocation
A Anandkumar, Y Liu, D Hsu, DP Foster, SM Kakade
Advances in Neural Information Processing Systems, 917-925, 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
A general agnostic active learning algorithm
S Dasgupta, D Hsu, C Monteleoni
Advances in neural information processing systems 20, 353-360, 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
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
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
A tail inequality for quadratic forms of subgaussian random vectors
D Hsu, SM Kakade, T Zhang
Arxiv preprint arXiv:1110.2842, 2011
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
Robust Matrix Decomposition with Sparse Corruptions
D Hsu, SM Kakade, T Zhang
Information Theory, IEEE Transactions on, 1-1, 2011
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
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
Certified robustness to adversarial examples with differential privacy
M Lecuyer, V Atlidakis, R Geambasu, D Hsu, S Jana
arXiv preprint arXiv:1802.03471, 2018
Agnostic active learning without constraints
A Beygelzimer, D Hsu, J Langford, T Zhang
Arxiv preprint arXiv:1006.2588, 2010
Random design analysis of ridge regression
D Hsu, SM Kakade, T Zhang
Conference on learning theory, 9.1-9.24, 2012
Loss minimization and parameter estimation with heavy tails
D Hsu, S Sabato
The Journal of Machine Learning Research 17 (1), 543-582, 2016
A parameter-free hedging algorithm
K Chaudhuri, Y Freund, D Hsu
Arxiv preprint arXiv:0903.2851, 2009
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