{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T07:22:17Z","timestamp":1759389737959,"version":"3.37.3"},"reference-count":34,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,9,17]],"date-time":"2023-09-17T00:00:00Z","timestamp":1694908800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,9,17]],"date-time":"2023-09-17T00:00:00Z","timestamp":1694908800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,17]]},"DOI":"10.1109\/mlsp55844.2023.10285917","type":"proceedings-article","created":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T17:55:41Z","timestamp":1698083741000},"page":"1-6","source":"Crossref","is-referenced-by-count":1,"title":["A New Non-Convex Framework to Improve Asymptotical Knowledge on Generic Stochastic Gradient Descent"],"prefix":"10.1109","author":[{"given":"Jean-Baptiste","family":"Fest","sequence":"first","affiliation":[{"name":"CVN, CentraleSupélec, Inria, Université Paris-Saclay, 9 rue Joliot Curie,Gif-sur-Yvette,France"}]},{"given":"Audrey","family":"Repetti","sequence":"additional","affiliation":[{"name":"Heriot-Watt University,School of Engineering & Physical Sciences, School of Mathematical & Computer Schiences,Edinburgh,UK,EH14 4AS"}]},{"given":"\u00c9milie","family":"Chouzenoux","sequence":"additional","affiliation":[{"name":"CVN, CentraleSupélec, Inria, Université Paris-Saclay, 9 rue Joliot Curie,Gif-sur-Yvette,France"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/EUSIPCO.2016.7760561"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/s10898-016-0405-9"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1137\/15M1053141"},{"key":"ref15","first-page":"1125","article-title":"Finito: A faster, permutable incremental gradient method for big data problems","author":"defazio","year":"2014","journal-title":"Proc of the International Conference on Machine Learning (ICML)"},{"key":"ref14","article-title":"SAGA: a fast incremental gradient method with support for non-strongly convex composite objectives","volume":"27","author":"defazio","year":"2014","journal-title":"Advances in neural information processing systems"},{"journal-title":"Fast Convergence of Stochastic Gradient Descent under A Strong Growth Condition","year":"2013","author":"schmidt","key":"ref31"},{"key":"ref30","first-page":"8469","article-title":"The impact of neural network overparameterization on gradient confusion and stochastic gradient descent","author":"sankararaman","year":"2020","journal-title":"Proc of the International Conference on Machine Learning (ICML)"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s10957-013-0465-7"},{"key":"ref33","first-page":"1195","article-title":"Fast and faster convergence of SGD for over-parameterized models and an accelerated perceptron","author":"vaswani","year":"2019","journal-title":"Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019)"},{"journal-title":"A kurdykalojasiewicz property for stochastic optimization algorithms in a non-convex setting","year":"2023","author":"chouzenoux","key":"ref10"},{"key":"ref32","first-page":"26","article-title":"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude","volume":"4","author":"tieleman","year":"2012","journal-title":"COURSERA Neural Networks for Machine Learning"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-007-0133-5"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1137\/040605266"},{"key":"ref17","article-title":"Stochastic optimization algorithms, non asymptotic and asymptotic behaviour","author":"gadat","year":"2017","journal-title":"Lecture Notes University of Toulouse"},{"key":"ref16","first-page":"2011","article-title":"Adaptive subgradient methods for online learning and stochastic optimization","volume":"12","author":"duchi","year":"0","journal-title":"Journal of Machine Learning Research"},{"key":"ref19","article-title":"Accelerating stochastic gradient descent using predictive variance reduction","volume":"26","author":"johnson","year":"2013","journal-title":"Advances in neural information processing systems"},{"key":"ref18","first-page":"1724","article-title":"How to escape saddle points efficiently","author":"jin","year":"2017","journal-title":"Proc of the International Conference on Machine Learning (ICML)"},{"journal-title":"Stochastic second-order optimization for over-parameterized machine learning models","year":"2020","author":"meng","key":"ref24"},{"key":"ref23","first-page":"87","article-title":"Une propriete topologique des sousensembles analytiques reels","volume":"117","author":"lojasiewicz","year":"1963","journal-title":"Les equations auxderivees partielles"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1137\/19M1277552"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/b98874"},{"article-title":"Better theory for SGD in the nonconvex world","year":"2020","author":"khaled","key":"ref20"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.5802\/aif.1638"},{"journal-title":"A method for stochastic optimization","year":"2014","author":"kingma","key":"ref21"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-604550-5.50015-8"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729586"},{"key":"ref29","article-title":"A stochastic gradient method with an exponential convergence rate for finite training sets","volume":"25","author":"roux","year":"2012","journal-title":"Advances in neural information processing systems"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-021-10705-5"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1137\/16M1080173"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s10957-022-02122-y"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1137\/S1052623497331063"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1287\/moor.1100.0449"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-013-0701-9"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1137\/050644641"}],"event":{"name":"2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)","start":{"date-parts":[[2023,9,17]]},"location":"Rome, Italy","end":{"date-parts":[[2023,9,20]]}},"container-title":["2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10285838\/10285858\/10285917.pdf?arnumber=10285917","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T19:03:28Z","timestamp":1699902208000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10285917\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,17]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1109\/mlsp55844.2023.10285917","relation":{},"subject":[],"published":{"date-parts":[[2023,9,17]]}}}