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Samuel B. Hopkins
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Algorithms and computational complexity: high-dimensional statistics, convex programming, linear and semidefinite programming hierarchies, approximation algorithms, combinatorial optimization, hardness of learning and approximation
Algorithms and computational complexity: high-dimensional statistics, convex programming, linear and semidefinite programming hierarchies, approximation algorithms, combinatorial optimization, hardness of learning and approximation
Papers21 papers
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ANNALS OF STATISTICS, no. 2 (2020): 1193-1213
NIPS 2020, (2020)
FOCS, pp.271-282, (2020)
STOC '20: 52nd Annual ACM SIGACT Symposium on Theory of Computing
Chicago
IL
..., pp.601-609, (2019)
FOCS, pp.943-953, (2019)
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), (2019): 6065-6075
COLT, (2019): 1649-1682
COLT, pp.1683-1722, (2019)
ADVANCES IN CRYPTOLOGY - EUROCRYPT 2019, PT I, (2018): 226-250
STOC '18: Symposium on Theory of Computing
Los Angeles
CA
USA
June, 20..., (2018): 1021-1034
arXiv: Statistics Theory, (2018)
ACM Trans. Algorithms, no. 3 (2018): 28:1-28:31
arXiv: Data Structures and Algorithms, (2017)
2017 IEEE 58TH ANNUAL SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE (FOCS), (2017): 720-731
STOC '16: Symposium on Theory of Computing
Cambridge
MA
USA
June, 2016, pp.178-191, (2016)
2016 IEEE 57TH ANNUAL SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE (FOCS), (2016): 428-437
computational learning theory, (2015): 956-1006
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