{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T16:49:40Z","timestamp":1770223780289,"version":"3.49.0"},"reference-count":79,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health","doi-asserted-by":"publisher","award":["1R01EB025020"],"award-info":[{"award-number":["1R01EB025020"]}],"id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004339","name":"Sanofi iDEA (innovation in Data Exploration and Analytics) Awards Initiative","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004339","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1109\/tnnls.2020.3029631","type":"journal-article","created":{"date-parts":[[2020,10,29]],"date-time":"2020-10-29T19:29:16Z","timestamp":1603999756000},"page":"1666-1680","source":"Crossref","is-referenced-by-count":10,"title":["Calibration and Uncertainty in Neural Time-to-Event Modeling"],"prefix":"10.1109","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0518-565X","authenticated-orcid":false,"given":"Paidamoyo","family":"Chapfuwa","sequence":"first","affiliation":[{"name":"Electrical and Computer Engineering Department, Duke University, Durham, NC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenyang","family":"Tao","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, Duke University, Durham, NC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunyuan","family":"Li","sequence":"additional","affiliation":[{"name":"Microsoft Research, Redmond, WA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9832-0904","authenticated-orcid":false,"given":"Irfan","family":"Khan","sequence":"additional","affiliation":[{"name":"Sanofi, Bridgewater, NJ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1346-2162","authenticated-orcid":false,"given":"Karen J.","family":"Chandross","sequence":"additional","affiliation":[{"name":"Sanofi, Bridgewater, NJ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael J.","family":"Pencina","sequence":"additional","affiliation":[{"name":"Biostatistics and Bioinformatics Department, Duke University, Durham, NC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lawrence","family":"Carin","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, Duke University, Durham, NC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4980-845X","authenticated-orcid":false,"given":"Ricardo","family":"Henao","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, Duke University, Durham, NC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-018-0029-1"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJM198707233170401"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011278"},{"key":"ref4","first-page":"3247","article-title":"Wasserstein learning of deep generative point process models","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Xiao"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939875"},{"key":"ref6","first-page":"6754","article-title":"The neural Hawkes process: A neurally self-modulating multivariate point process","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Mei"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12072"},{"key":"ref8","first-page":"10781","article-title":"Learning temporal point processes via reinforcement learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Li"},{"key":"ref9","first-page":"3168","article-title":"Deep reinforcement learning of marked temporal point processes","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Upadhyay"},{"key":"ref10","first-page":"2030","article-title":"A multitask point process predictive model","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Lian"},{"key":"ref11","volume-title":"Survival Analysis","author":"Kleinbaum","year":"2010"},{"key":"ref12","first-page":"1","article-title":"Adversarial time-to-event modeling","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Chapfuwa"},{"key":"ref13","first-page":"244","article-title":"Deep survival analysis: Nonparametrics and missingness","volume-title":"Proc. Mach. Learn. Healthcare Conf.","author":"Miscouridou"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1002\/sim.4780030207"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-4380-9_37"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1002\/sim.4780111409"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1002\/sim.2059"},{"key":"ref18","article-title":"Deep survival: A deep Cox proportional hazards network","volume-title":"Proc. Int. Conf. Mach. Learn. Comput. Biol. Workshop","author":"Katzman"},{"key":"ref19","first-page":"1","article-title":"Deep survival analysis","volume-title":"Proc. Mach. Learn. Healthcare Conf.","author":"Ranganath"},{"key":"ref20","article-title":"Countdown regression: Sharp and calibrated survival predictions","volume-title":"arXiv:1806.08324","author":"Avati","year":"2018"},{"key":"ref21","first-page":"5021","article-title":"Gaussian processes for survival analysis","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Fern\u00e1ndez"},{"key":"ref22","first-page":"2326","article-title":"Deep multi-task Gaussian processes for survival analysis with competing risks","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Alaa"},{"key":"ref23","first-page":"596","article-title":"Temporal quilting for survival analysis","volume-title":"Proc. 22nd Int. Conf. Artif. Intell. Statist. (AISTATS)","author":"Lee"},{"key":"ref24","first-page":"1845","article-title":"Learning patient-specific cancer survival distributions as a sequence of dependent regressors","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Yu"},{"key":"ref25","article-title":"Deep neural networks for survival analysis based on a multi-task framework","volume-title":"arXiv:1801.05512","author":"Fotso","year":"2018"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11842"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.2307\/2987588"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.2307\/2287720"},{"key":"ref29","first-page":"1321","article-title":"On calibration of modern neural networks","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Guo"},{"key":"ref30","first-page":"2796","article-title":"Accurate uncertainties for deep learning using calibrated regression","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Kuleshov"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2719028"},{"key":"ref32","first-page":"5002","article-title":"Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Zhang"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2"},{"key":"ref34","article-title":"Effective ways to build and evaluate individual survival distributions","volume-title":"arXiv:1811.11347","author":"Haider","year":"2018"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1097\/EDE.0b013e3181c30fb2"},{"key":"ref36","first-page":"2672","article-title":"Generative adversarial nets","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Goodfellow"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.2307\/2281868"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1002\/9780470258019"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.2307\/2286474"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176342705"},{"issue":"33","key":"ref41","first-page":"1","article-title":"A report on the natural duration of cancer","volume-title":"Rep. Natural Duration Cancer","volume":"33","author":"Greenwood","year":"1926"},{"key":"ref42","first-page":"1209","article-title":"On ranking in survival analysis: Bounds on the concordance index","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Steck"},{"key":"ref43","first-page":"2234","article-title":"Improved techniques for training GANs","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Salimans"},{"key":"ref44","first-page":"4006","article-title":"Adversarial feature matching for text generation","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref45","article-title":"Image-to-image translation with conditional adversarial networks","volume-title":"arXiv:1611.07004","author":"Isola","year":"2016"},{"key":"ref46","first-page":"1","article-title":"Progressive growing of GANs for improved quality, stability, and variation","volume-title":"Proc. ICLR","author":"Karras"},{"key":"ref47","article-title":"Conditional generative adversarial nets","volume-title":"arXiv:1411.1784","author":"Mirza","year":"2014"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/bf00992696"},{"key":"ref49","first-page":"1278","article-title":"Stochastic backpropagation and approximate inference in deep generative models","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Rezende"},{"key":"ref50","first-page":"1","article-title":"Auto-encoding variational Bayes","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Kingma"},{"key":"ref51","first-page":"1","article-title":"Backpropagation through the void: Optimizing control variates for black-box gradient estimation","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Grathwohl"},{"key":"ref52","first-page":"1","article-title":"Improving sequence-to-sequence learning via optimal transport","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Chen"},{"key":"ref53","first-page":"1","article-title":"Improving GANs using optimal transport","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Salimans"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2695801"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-71050-9"},{"key":"ref56","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1023\/A:1026543900054","article-title":"The Earth mover\u2019s distance as a metric for image retrieval","volume":"40","author":"Rubner","year":"2000","journal-title":"Int. J. Comput. Vis."},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.5555\/3045118.3045336"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d15-1044"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v033.i01"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1214\/11-AOS911"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMp1500523"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1214\/08-AOAS169"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1977.10480613"},{"key":"ref64","article-title":"Deep learning for patient-specific kidney graft survival analysis","volume-title":"arXiv:1705.10245","author":"Luck","year":"2017"},{"key":"ref65","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","volume-title":"Proc. 13th Int. Conf. Artif. Intell. Statist.","author":"Glorot"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1016\/j.mayocp.2012.03.009"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.7326\/0003-4819-122-3-199502010-00007"},{"issue":"07-6215","key":"ref68","first-page":"193","article-title":"Cancer survival among adults: US SEER program, 1988\u20132001","author":"Ries","year":"2007","journal-title":"Patient Tumor Characteristics SEER Survival Monograph Publication"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1093\/sleep\/20.12.1077"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1145\/3233547.3233725"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1201\/9781315140919"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1198\/016214506000001437"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0450(1973)012<0595:ANVPOT>2.0.CO;2"},{"key":"ref74","volume-title":"Survival Analysis: Techniques for Censored and Truncated Data","author":"Klein","year":"2005"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0258(19970130)16:2<215::AID-SIM481>3.0.CO;2-J"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1177\/009286150203600312"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1002\/sim.4780140108"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1126\/scitranslmed.3005974"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1038\/s41698-017-0022-1"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10091960\/09244076.pdf?arnumber=9244076","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T22:50:02Z","timestamp":1704840602000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9244076\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4]]},"references-count":79,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2020.3029631","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4]]}}}