{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T19:08:04Z","timestamp":1779131284885,"version":"3.51.4"},"reference-count":61,"publisher":"Association for Computing Machinery (ACM)","issue":"3","funder":[{"name":"DARPA ASKEM Award","award":["HR00112220042"],"award-info":[{"award-number":["HR00112220042"]}]},{"name":"ARPA-H Biomedical Data Fabric project","award":["none"],"award-info":[{"award-number":["none"]}]},{"name":"United States\u2013Israel Binational Science Foundation","award":["2024101"],"award-info":[{"award-number":["2024101"]}]},{"DOI":"10.13039\/501100003977","name":"Israel Science Foundation","doi-asserted-by":"publisher","award":["934\\\/25"],"award-info":[{"award-number":["934\\\/25"]}],"id":[{"id":"10.13039\/501100003977","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000183","name":"U.S. Army Research Office","doi-asserted-by":"crossref","award":["W911NF-25-1-0254"],"award-info":[{"award-number":["W911NF-25-1-0254"]}],"id":[{"id":"10.13039\/100000183","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2026,5,18]]},"abstract":"<jats:p>\n                    Data discretization, the conversion of numeric attributes into categorical bins, underpins many data-driven tasks, from visualization and causal analysis to symbolic learning.\n                    <jats:italic toggle=\"yes\">Interpretable partitions<\/jats:italic>\n                    make results easier to communicate and act upon. Yet, a fundamental tension underlies this discretization process: partitions that are interpretable to humans often differ from those that maximize utility performance (e.g., causal analysis). Despite the abundance of discretization techniques, reconciling this utility-semantics trade-off remains an open challenge.\n                  <\/jats:p>\n                  <jats:p>\n                    In this work, we formally define the interpretable attribute discretization problem and introduce a reinforcement-learning-based framework that jointly optimizes utility and semantic fidelity. Our approach\n                    <jats:italic toggle=\"yes\">learns a partition selection policy<\/jats:italic>\n                    that can navigate a vast space of candidates and accurately estimate the optimal partitions by strategically sampling only 200 candidates out of millions.\n                  <\/jats:p>\n                  <jats:p>\n                    We measure success by the average Hausdorff distance between the estimated and true\n                    <jats:italic toggle=\"yes\">Pareto frontier<\/jats:italic>\n                    of the utility\u2013semantics trade-off. On seven datasets across four tasks (visualization, modeling, imputation, causal analysis) and multiple semantic measures, our method achieves the lowest overall error, typically\n                    <jats:bold>2\u20134<\/jats:bold>\n                    \u00d7\n                    <jats:bold>smaller<\/jats:bold>\n                    than strong alternatives, yielding a more accurate Pareto frontier that reveals the complete utility\u2013semantics trade-off.\n                  <\/jats:p>","DOI":"10.1145\/3802075","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T18:19:16Z","timestamp":1779128356000},"page":"1-27","source":"Crossref","is-referenced-by-count":0,"title":["Interpretable Attribute Discretization"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1349-1376","authenticated-orcid":false,"given":"Eugenie","family":"Lai","sequence":"first","affiliation":[{"name":"MIT CSAIL, Cambridge, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9122-3696","authenticated-orcid":false,"given":"Inbal","family":"Croitoru","sequence":"additional","affiliation":[{"name":"Technion, Haifa, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0031-5550","authenticated-orcid":false,"given":"Brit","family":"Youngmann","sequence":"additional","affiliation":[{"name":"Technion, Haifa, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2529-4036","authenticated-orcid":false,"given":"Sainyam","family":"Galhotra","sequence":"additional","affiliation":[{"name":"Cornell University, Ithaca, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5187-3499","authenticated-orcid":false,"given":"El Kindi","family":"Rezig","sequence":"additional","affiliation":[{"name":"The University of Utah, Salt Lake City, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6122-0590","authenticated-orcid":false,"given":"Michael","family":"Cafarella","sequence":"additional","affiliation":[{"name":"MIT CSAIL, Cambridge, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,5,18]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"[n.d.]. 19000 Spotify Songs Dataset. https:\/\/www.kaggle.com\/datasets\/edalrami\/19000-spotify-songs. 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