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Shai Ben-David

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Shai Ben-David
Born
Jerusalem, Israel
NationalityIsraeli-Canadian
Alma materHebrew University of Jerusalem (PhD)
Known forResearch in theoretical machine learning, learning theory, online algorithms
AwardsNeurIPS Best Paper Award
Scientific career
FieldsTheoretical machine learning
InstitutionsUniversity of Waterloo
Doctoral advisorSaharon Shelah

Shai Ben-David is an Israeli-Canadian computer scientist and professor at the University of Waterloo. He is known for his research in theoretical machine learning.[1]

Biography

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Shai Ben-David grew up in Jerusalem, Israel and received a Ph.D. in mathematics from the Hebrew University of Jerusalem,[2] where he was advised by Saharon Shelah.[3][2] He held postdoctoral positions in mathematics and computer science at the University of Toronto. He was a professor of computer science at the Technion and also held visiting positions at the Australian National University and Cornell University.[4]

He has been a professor of computer science at the University of Waterloo since 2004.

Selected publications and awards

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Ben-David has written highly cited papers on learning theory and online algorithms.[5][6][7][8][9] He is a co-author, with Shai Shalev-Shwartz, of the book "Understanding Machine Learning: From Theory to Algorithms"(Cambridge University Press, 2014).[1]

He received the best paper award at NeurIPS 2018.[10] for work on sample complexity of distribution learning problems.[11] He was the President of the Association for Computational Learning from 2009 to 2011.[12]

Awards

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Publications

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  • Shalev-Shwartz, Shai; Ben-David, Shai (2014). Understanding machine learning: From theory to algorithms. Cambridge University Press.
  • Ben-David, Shai; Blitzer, John; Crammer, Koby; Kulesza, Alex; Pereira, Fernando; Wortman Vaughan, Jennifer (2010). "A theory of learning from different domains". Machine Learning. 79. Springer US: 151–175.
  • Ben-David, Shai; Blitzer, John; Crammer, Koby; Pereira, Fernando (2006). "Analysis of representations for domain adaptation". Advances in Neural Information Processing Systems. 19.
  • Kifer, Daniel; Ben-David, Shai; Gehrke, Johannes (2004). "Detecting change in data streams". VLDB. 4.

References

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  1. ^ a b Shalev-Shwartz, Shai; Ben-David, Shai (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge: Cambridge University Press. ISBN 978-1-107-05713-5.
  2. ^ a b "Shai Ben-David at the Mathematics Genealogy Project".
  3. ^ "ACML 2018 Main/Speakers". www.acml-conf.org. Retrieved 2021-04-26.
  4. ^ "Shai Ben-David | Simons Institute for the Theory of Computing". simons.berkeley.edu. Retrieved 2021-04-10.
  5. ^ Ben-David, Shai; Blitzer, John; Crammer, Koby; Kulesza, Alex; Pereira, Fernando; Vaughan, Jennifer Wortman (2010-05-01). "A theory of learning from different domains". Machine Learning. 79 (1): 151–175. doi:10.1007/s10994-009-5152-4. ISSN 1573-0565.
  6. ^ Schölkopf, Bernhard; Platt, John; Hofmann, Thomas (2007). Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference. MIT Press. ISBN 978-0-262-19568-3.
  7. ^ VLDB (2004-10-08). Proceedings 2004 VLDB Conference: The 30th International Conference on Very Large Databases (VLDB). Elsevier. ISBN 978-0-08-053979-9.
  8. ^ Ben-David, S.; Borodin, A.; Karp, R.; Tardos, G.; Wigderson, A. (1994-01-01). "On the power of randomization in on-line algorithms". Algorithmica. 11 (1): 2–14. doi:10.1007/BF01294260. ISSN 1432-0541. S2CID 26771869.
  9. ^ Alon, Noga; Ben-David, Shai; Cesa-Bianchi, Nicolò; Haussler, David (1997-07-01). "Scale-sensitive dimensions, uniform convergence, and learnability". Journal of the ACM. 44 (4): 615–631. doi:10.1145/263867.263927. ISSN 0004-5411.
  10. ^ "Professor Shai Ben-David and colleagues win best paper award at NeurIPS 2018". Cheriton School of Computer Science. 2018-12-03. Retrieved 2021-04-10.
  11. ^ "Nearly Tight Sample Complexity Bounds for Learning Mixtures of Gaussians via Sample Compression Schemes" (PDF).
  12. ^ "Shai Ben-David". CIFAR. Retrieved 2021-04-10.
  13. ^ "Shai Ben-David". awards.acm.org. Retrieved 2024-01-26.
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