{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T07:22:25Z","timestamp":1774941745946,"version":"3.50.1"},"reference-count":21,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T00:00:00Z","timestamp":1626048000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T00:00:00Z","timestamp":1626048000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100004063","name":"Knut and Alice Wallenberg Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004063","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,12]]},"DOI":"10.1109\/isit45174.2021.9517731","type":"proceedings-article","created":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T16:52:42Z","timestamp":1630515162000},"page":"952-957","source":"Crossref","is-referenced-by-count":2,"title":["Fast-Rate Loss Bounds via Conditional Information Measures with Applications to Neural Networks"],"prefix":"10.1109","author":[{"given":"Fredrik","family":"Hellstr\u00f6m","sequence":"first","affiliation":[{"name":"Chalmers University of Technology,Department of Electrical Engineering,Gothenburg,Sweden,41296"}]},{"given":"Giuseppe","family":"Durisi","sequence":"additional","affiliation":[{"name":"Chalmers University of Technology,Department of Electrical Engineering,Gothenburg,Sweden,41296"}]}],"member":"263","reference":[{"key":"ref10","author":"esposito","year":"2019","journal-title":"Generalization error bounds via Rènyif-divergences and maximal leakage"},{"key":"ref11","author":"mcallester","year":"2013","journal-title":"A PAC-Bayesian tutorial with a dropout bound"},{"key":"ref12","article-title":"Fast-rate PAC-Bayes generalization bounds via shifted Rademacher processes","author":"yang","year":"2019","journal-title":"Proc Conf Neural Inf Process Syst (NeurIPS)"},{"key":"ref13","first-page":"1","article-title":"Fast rates for general unbounded loss functions: from ERM to generalized Bayes","volume":"83","author":"gr\u00fcnwald","year":"2020","journal-title":"J of Mach Learn Res"},{"key":"ref14","article-title":"Reasoning about generalization via conditional mutual information","author":"steinke","year":"2020","journal-title":"Proc Conf Learn Theory (COLT)"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2020.3040992"},{"key":"ref16","author":"dziugaite","year":"2020","journal-title":"On the Role of Data in PAC-Bayes Bounds"},{"key":"ref17","author":"polyanskiy","year":"2019","journal-title":"Lecture Notes on Information Theory"},{"key":"ref18","author":"haghifam","year":"2020","journal-title":"Sharpened Generalization Bounds Based on Conditional Mutual Information and an Application to Noisy Iterative Algorithms"},{"key":"ref19","article-title":"Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data","author":"dziugaite","year":"2017","journal-title":"Proc Conf Uncertainty in Artif Intell (UAI)"},{"key":"ref4","article-title":"Controlling bias in adaptive data analysis using information theory","author":"russo","year":"2016","journal-title":"Proc Artif Intell Statist (AISTATS)"},{"key":"ref3","author":"guedj","year":"2019","journal-title":"A primer on PAC-Bayesian learning"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2019.8849590"},{"key":"ref5","article-title":"Information-theoretic analysis of generalization capability of learning algorithms","author":"xu","year":"2017","journal-title":"Proc Conf Neural Inf Process Syst (NeurIPS)"},{"key":"ref8","article-title":"Information-theoretic generalization bounds for SGLD via data-dependent estimates","author":"negrea","year":"2019","journal-title":"Proc Conf Neural Inf Process Syst (NeurIPS)"},{"key":"ref7","article-title":"Chaining mutual information and tightening generalization bounds","author":"asadi","year":"2018","journal-title":"Proc Conf Neural Inf Process Syst (NeurIPS)"},{"key":"ref2","article-title":"PAC-Bayesian Supervised Classification: The Thermodynamics of Statistical Learning","volume":"56","author":"catoni","year":"2007","journal-title":"IMS Lecture Notes-Monograph Series"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/279943.279989"},{"key":"ref9","first-page":"25","article-title":"Learners that use little information","volume":"83","author":"bassily","year":"2018","journal-title":"J of Mach Learn Res"},{"key":"ref20","author":"hellstr\u00f6m","year":"2020","journal-title":"Fast-rate loss bounds via conditional information measures with applications to neural networks"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1017\/9781108627771"}],"event":{"name":"2021 IEEE International Symposium on Information Theory (ISIT)","location":"Melbourne, Australia","start":{"date-parts":[[2021,7,12]]},"end":{"date-parts":[[2021,7,20]]}},"container-title":["2021 IEEE International Symposium on Information Theory (ISIT)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9517708\/9517709\/09517731.pdf?arnumber=9517731","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T20:35:25Z","timestamp":1773347725000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9517731\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,12]]},"references-count":21,"URL":"https:\/\/doi.org\/10.1109\/isit45174.2021.9517731","relation":{},"subject":[],"published":{"date-parts":[[2021,7,12]]}}}