Shai Ben-David Explained

Shai Ben-David
Birth Place:Jerusalem, Israel
Nationality:Israeli-Canadian
Fields:Theoretical machine learning
Workplaces:University of Waterloo
Alma Mater:Hebrew University of Jerusalem (PhD)
Doctoral Advisor:Saharon Shelah
Known For:Research in theoretical machine learning, learning theory, online algorithms
Awards:NeurIPS Best Paper Award

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

Shai Ben-David grew up in Jerusalem, Israel and received a Ph.D. in mathematics from the Hebrew University of Jerusalem, where he was advised by Saharon Shelah.[2] [3] 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

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).

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

Publications

External links

Notes and References

  1. Book: Shalev-Shwartz. Shai. Understanding Machine Learning: From Theory to Algorithms. Ben-David. Shai. 2014. Cambridge University Press. 978-1-107-05713-5. Cambridge.
  2. Web site: ACML 2018 Main/Speakers. 2021-04-26. www.acml-conf.org.
  3. Web site: Shai Ben-David at the Mathematics Genealogy Project.
  4. Web site: Shai Ben-David Simons Institute for the Theory of Computing. 2021-04-10. simons.berkeley.edu.
  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. en. 79. 1. 151–175. 10.1007/s10994-009-5152-4. 1573-0565. free.
  6. Book: Schölkopf. Bernhard. Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference. Platt. John. Hofmann. Thomas. 2007. MIT Press. 978-0-262-19568-3. en.
  7. Book: VLDB. Proceedings 2004 VLDB Conference: The 30th International Conference on Very Large Databases (VLDB). 2004-10-08. Elsevier. 978-0-08-053979-9. en.
  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. en. 11. 1. 2–14. 10.1007/BF01294260. 26771869 . 1432-0541.
  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. 10.1145/263867.263927. 0004-5411. free.
  10. Web site: 2018-12-03. Professor Shai Ben-David and colleagues win best paper award at NeurIPS 2018. 2021-04-10. Cheriton School of Computer Science. en.
  11. Web site: Nearly Tight Sample Complexity Bounds for Learning Mixtures of Gaussians via Sample Compression Schemes.
  12. Web site: Shai Ben-David. 2021-04-10. CIFAR. en-US.
  13. Web site: Shai Ben-David . 2024-01-26 . awards.acm.org . en.

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