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Learn.: Sci. Technol."],"published-print":{"date-parts":[[2025,12,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Key science questions, such as galaxy distance estimation and weather forecasting, often require knowing the full predictive distribution of a target variable\n                    <jats:italic>Y<\/jats:italic>\n                    given complex inputs\n                    <jats:bold>X<\/jats:bold>\n                    . Despite recent advances in machine learning and physics-based models, it remains challenging to assess whether an initial model is calibrated for all\n                    <jats:bold>x<\/jats:bold>\n                    , and when needed, to reshape the densities of\n                    <jats:italic>y<\/jats:italic>\n                    toward \u2018instance-wise\u2019 calibration. This paper introduces the local amortized diagnostics and reshaping of conditional densities (LADaR) framework and proposes a new computationally efficient algorithm (\n                    <jats:monospace>Cal-PIT<\/jats:monospace>\n                    ) that produces interpretable local diagnostics and provides a mechanism for adjusting conditional density estimates (CDEs).\n                    <jats:monospace>Cal-PIT<\/jats:monospace>\n                    learns a single interpretable local probability\u2013probability map from calibration data that identifies where and how the initial model is miscalibrated across feature space, which can be used to morph CDEs such that they are well-calibrated. We illustrate the LADaR framework on synthetic examples, including probabilistic forecasting from image sequences, akin to predicting storm wind speed from satellite imagery. Our main science application involves estimating the probability density functions of galaxy distances given photometric data, where\n                    <jats:monospace>Cal-PIT<\/jats:monospace>\n                    achieves better instance-wise calibration than all 11 other literature methods in a benchmark data challenge, demonstrating its utility for next-generation cosmological analyzes\n                    <jats:sup>9<\/jats:sup>\n                    <jats:fn id=\"mlstae1f05fn2\">\n                      <jats:label>9<\/jats:label>\n                      <jats:p>\n                        Code available as a Python package here:\n                        <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/lee-group-cmu\/Cal-PIT\">https:\/\/github.com\/lee-group-cmu\/Cal-PIT<\/jats:ext-link>\n                        .\n                      <\/jats:p>\n                    <\/jats:fn>\n                    .\n                  <\/jats:p>","DOI":"10.1088\/2632-2153\/ae1f05","type":"journal-article","created":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T08:47:41Z","timestamp":1763023661000},"page":"045058","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Towards instance-wise calibration: local amortized diagnostics and reshaping of conditional densities (LADaR)"],"prefix":"10.1088","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5665-7912","authenticated-orcid":true,"given":"Biprateep","family":"Dey","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8085-5890","authenticated-orcid":true,"given":"Brett H","family":"Andrews","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8684-2222","authenticated-orcid":false,"given":"Jeffrey A","family":"Newman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0379-9690","authenticated-orcid":false,"given":"Rafael","family":"Izbicki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4430-7621","authenticated-orcid":true,"given":"Ann B","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,12,2]]},"reference":[{"key":"mlstae1f05bib1","article-title":"The wide field infrared survey telescope: 100 hubbles for the 2020s","author":"Akeson","year":"2019","type":"preprint"},{"key":"mlstae1f05bib2","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1214\/07-AOAS111","type":"journal-article","article-title":"Probabilistic projections of HIV prevalence using Bayesian melding","volume":"1","author":"Alkema","year":"2007","journal-title":"Ann. 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