{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T07:22:41Z","timestamp":1774941761543,"version":"3.50.1"},"reference-count":8,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,10,17]],"date-time":"2021-10-17T00:00:00Z","timestamp":1634428800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,10,17]],"date-time":"2021-10-17T00:00:00Z","timestamp":1634428800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,10,17]],"date-time":"2021-10-17T00:00:00Z","timestamp":1634428800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,17]]},"DOI":"10.1109\/itw48936.2021.9611483","type":"proceedings-article","created":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T21:46:27Z","timestamp":1637703987000},"page":"1-6","source":"Crossref","is-referenced-by-count":1,"title":["Lower Bounds on the Expected Excess Risk Using Mutual Information"],"prefix":"10.1109","author":[{"given":"M. Bora","family":"Dogan","sequence":"first","affiliation":[]},{"given":"Michael","family":"Gastpar","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ITW46852.2021.9457642"},{"key":"ref3","first-page":"9","article-title":"Reasoning About Generalization via Conditional Mutual Information","volume":"125","author":"steinke","year":"2020","journal-title":"Proceedings of Thirty Third Conference on Learning Theory"},{"key":"ref6","article-title":"Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data","author":"dziugaite","year":"2017","journal-title":"arXiv preprint arXiv 1703 10847"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2018.8437571"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2020.2991139"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781107298019"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2021.3085190"},{"key":"ref1","first-page":"2524","article-title":"Information-theoretic analysis of generalization capability of learning algorithms","author":"xu","year":"2017","journal-title":"Advances in neural information processing systems"}],"event":{"name":"2021 IEEE Information Theory Workshop (ITW)","location":"Kanazawa, Japan","start":{"date-parts":[[2021,10,17]]},"end":{"date-parts":[[2021,10,21]]}},"container-title":["2021 IEEE Information Theory Workshop (ITW)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9611366\/9611357\/09611483.pdf?arnumber=9611483","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T16:52:06Z","timestamp":1652201526000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9611483\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,17]]},"references-count":8,"URL":"https:\/\/doi.org\/10.1109\/itw48936.2021.9611483","relation":{},"subject":[],"published":{"date-parts":[[2021,10,17]]}}}