2016 - Fellow of Alfred P. Sloan Foundation
His primary scientific interests are in Algorithm, Artificial intelligence, Applied mathematics, Singular value decomposition and Mathematical optimization. His study in Algorithm is interdisciplinary in nature, drawing from both Unsupervised learning, Generalization error, Mixture model, Hidden Markov model and VC dimension. Within one scientific family, Daniel Hsu focuses on topics pertaining to Machine learning under Artificial intelligence, and may sometimes address concerns connected to Structure.
His Applied mathematics research is multidisciplinary, incorporating perspectives in Tensor product network, Estimator, Latent variable and Least squares. His research in Singular value decomposition intersects with topics in Symmetric tensor and Combinatorics. Daniel Hsu has researched Mathematical optimization in several fields, including Matrix decomposition, Sparse matrix, Robustness and Matrix completion.
Daniel Hsu mainly investigates Artificial intelligence, Machine learning, Applied mathematics, Algorithm and Mathematical optimization. Daniel Hsu undertakes interdisciplinary study in the fields of Artificial intelligence and Context through his research. His studies deal with areas such as Classifier and Structure as well as Machine learning.
He combines subjects such as Mixture model, Estimator, Symmetric tensor and Covariance with his study of Applied mathematics. Daniel Hsu has included themes like Generalization, Unsupervised learning, Regret and Hidden Markov model in his Algorithm study. He studied Mathematical optimization and Matrix decomposition that intersect with Singular value decomposition.
His primary areas of investigation include Artificial intelligence, Machine learning, Combinatorics, Linear regression and Sample size determination. As part of his studies on Artificial intelligence, Daniel Hsu frequently links adjacent subjects like Pattern recognition. He interconnects Classifier, Structure and Class in the investigation of issues within Machine learning.
His Structure study incorporates themes from Active learning and Simple. Combinatorics is closely attributed to Estimator in his research. His Least squares research includes themes of Applied mathematics and Double descent.
Daniel Hsu spends much of his time researching Artificial intelligence, Machine learning, Algorithm, Artificial neural network and Sample size determination. His research integrates issues of Structure and Differential privacy in his study of Artificial intelligence. His research investigates the connection between Machine learning and topics such as Search engine indexing that intersect with issues in Pair distribution function.
The study incorporates disciplines such as Regression and Interpolation in addition to Algorithm. His biological study spans a wide range of topics, including Spurious relationship, Simple and Structure. The concepts of his Sample size determination study are interwoven with issues in Linear regression, Double descent, Norm, Applied mathematics and Least squares.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Tensor decompositions for learning latent variable models
Animashree Anandkumar;Rong Ge;Daniel Hsu;Sham M. Kakade.
Journal of Machine Learning Research (2014)
Hierarchical sampling for active learning
Sanjoy Dasgupta;Daniel Hsu.
international conference on machine learning (2008)
Reconciling modern machine-learning practice and the classical bias-variance trade-off.
Mikhail Belkin;Daniel Hsu;Siyuan Ma;Soumik Mandal.
Proceedings of the National Academy of Sciences of the United States of America (2019)
Multi-Label Prediction via Compressed Sensing
John Langford;Tong Zhang;Daniel J. Hsu;Sham M Kakade.
neural information processing systems (2009)
Multi-Label Prediction via Compressed Sensing
Daniel Hsu;Sham M. Kakade;John Langford;Tong Zhang.
arXiv: Learning (2009)
A general agnostic active learning algorithm
Sanjoy Dasgupta;Claire Monteleoni;Daniel J. Hsu.
neural information processing systems (2007)
Learning mixtures of spherical gaussians: moment methods and spectral decompositions
Daniel Hsu;Sham M. Kakade.
conference on innovations in theoretical computer science (2013)
Certified Robustness to Adversarial Examples with Differential Privacy
Mathias Lecuyer;Vaggelis Atlidakis;Roxana Geambasu;Daniel Hsu.
ieee symposium on security and privacy (2019)
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits
Alekh Agarwal;Daniel Hsu;Satyen Kale;John Langford.
international conference on machine learning (2014)
A Method of Moments for Mixture Models and Hidden Markov Models
Animashree Anandkumar;Daniel J. Hsu;Sham M. Kakade.
conference on learning theory (2012)
Profile was last updated on December 6th, 2021.
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