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ACM"],"published-print":{"date-parts":[[2024,10,31]]},"abstract":"<jats:p>\n            Algorithms often have tunable parameters that impact performance metrics such as runtime and solution quality. For many algorithms used in practice, no parameter settings admit meaningful worst-case bounds, so the parameters are made available for the user to tune. Alternatively, parameters may be tuned implicitly within the proof of a worst-case approximation ratio or runtime bound. Worst-case instances, however, may be rare or nonexistent in practice. A growing body of research has demonstrated that a data-driven approach to parameter tuning can lead to significant improvements in performance. This approach uses a\n            <jats:italic>training set<\/jats:italic>\n            of problem instances sampled from an unknown, application-specific distribution and returns a parameter setting with strong average performance on the training set.\n          <\/jats:p>\n          <jats:p>\n            We provide techniques for deriving\n            <jats:italic>generalization guarantees<\/jats:italic>\n            that bound the difference between the algorithm\u2019s average performance over the training set and its expected performance on the unknown distribution. Our results apply no matter how the parameters are tuned, be it via an automated or manual approach. The challenge is that for many types of algorithms, performance is a volatile function of the parameters: slightly perturbing the parameters can cause a large change in behavior. Prior research\u00a0[e.g.,\n            <jats:xref ref-type=\"bibr\">12<\/jats:xref>\n            ,\n            <jats:xref ref-type=\"bibr\">16<\/jats:xref>\n            ,\n            <jats:xref ref-type=\"bibr\">20<\/jats:xref>\n            ,\n            <jats:xref ref-type=\"bibr\">62<\/jats:xref>\n            ] has proved generalization bounds by employing case-by-case analyses of greedy algorithms, clustering algorithms, integer programming algorithms, and selling mechanisms. We streamline these analyses with a general theorem that applies whenever an algorithm\u2019s performance is a piecewise-constant, piecewise-linear, or\u2014more generally\u2014\n            <jats:italic>piecewise-structured<\/jats:italic>\n            function of its parameters. Our results, which are tight up to logarithmic factors in the worst case, also imply novel bounds for configuring dynamic programming algorithms from computational biology.\n          <\/jats:p>","DOI":"10.1145\/3676278","type":"journal-article","created":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T11:09:33Z","timestamp":1722251373000},"page":"1-58","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["How Much Data Is Sufficient to Learn High-Performing Algorithms?"],"prefix":"10.1145","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9525-0103","authenticated-orcid":false,"given":"Maria-Florina","family":"Balcan","sequence":"first","affiliation":[{"name":"Computer Science, Carnegie Mellon University, Pittsburgh, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4110-4431","authenticated-orcid":false,"given":"Dan","family":"Deblasio","sequence":"additional","affiliation":[{"name":"Computational biology, Carnegie Mellon University, Pittsburgh, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1271-307X","authenticated-orcid":false,"given":"Travis","family":"Dick","sequence":"additional","affiliation":[{"name":"Google Inc New York, New York, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0118-5516","authenticated-orcid":false,"given":"Carl","family":"Kingsford","sequence":"additional","affiliation":[{"name":"Computational biology, Carnegie Mellon University, Pittsburgh, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8861-9366","authenticated-orcid":false,"given":"Tuomas","family":"Sandholm","sequence":"additional","affiliation":[{"name":"Computer science, Carnegie Mellon University, Pittsburgh, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4891-1367","authenticated-orcid":false,"given":"Ellen","family":"Vitercik","sequence":"additional","affiliation":[{"name":"Management Science &amp; Engineering; Computer science, Stanford University, Stanford, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,10,5]]},"reference":[{"issue":"1","key":"e_1_3_3_2_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12532-008-0001-1","article-title":"SCIP: Solving constraint integer programs","volume":"1","author":"Achterberg Tobias","year":"2009","unstructured":"Tobias Achterberg. 2009. 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