How Much Data Is Sufficient to Learn High-Performing Algorithms? is a research paper published in Journal of the ACM (2024). On theSindex it has a DataRank of 0. It has been cited 14 times.
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Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative
Grant: GBMF4554
US National Institutes of Health
Grant: R01GM122935
US National Science Foundation
Grant: Graduate Research Fellowship and CCF-2338226 to E.V. and grants IIS-1901403 to M.B. and T.S., IIS-1618714, CCF-1535967, CCF-1910321, and SES-1919453 to M.B., RI-2312342, IIS-1718457, IIS-1617590, and CCF-1733556 to T.S., and DBI-1937540 to C.K.
US Army Research Office
Grant: W911NF2210266, W911NF-17-1-0082 and W911NF2010081 to T.S.
Vannevar Bush Faculty Fellowship to T.S., the Office of Naval Research
Grant: N00014-23-1-2876 to T.S.
Defense Advanced Research Projects Agency under cooperative agreement
Grant: HR00112020003 to M.B.
National Science Foundation
Grant: 1535967
AitF: FULL: From Worst-Case to Realistic-Case Analysis for Large Scale Machine Learning Algorithms
National Science Foundation
Grant: 1718457
RI: Small: New Computational Techniques and Market Designs for Kidney Exchanges and Other Barter Markets
National Science Foundation
Grant: 1901403
RI: Medium: Learning to Search: Provable Guarantees and Applications
National Institutes of Health
Grant: 5R01GM122935-04
Data Discovery: Computational Methods for Searching Short-Read Sequencing Experiments
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