{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:13:13Z","timestamp":1740132793575,"version":"3.37.3"},"reference-count":56,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000850","name":"American Society for Engineering Education","doi-asserted-by":"publisher","award":["SMART"],"award-info":[{"award-number":["SMART"]}],"id":[{"id":"10.13039\/100000850","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Signal Process."],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/tsp.2022.3173146","type":"journal-article","created":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T19:35:00Z","timestamp":1651865700000},"page":"3148-3164","source":"Crossref","is-referenced-by-count":2,"title":["Sparse Representations of Positive Functions via First- and Second-Order Pseudo-Mirror Descent"],"prefix":"10.1109","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6398-7402","authenticated-orcid":false,"given":"Abhishek","family":"Chakraborty","sequence":"first","affiliation":[{"name":"NetApp, Bangaluru, Karnataka, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4508-0062","authenticated-orcid":false,"given":"Ketan","family":"Rajawat","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Indian Institute of Technology, Kanpur, UP, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2447-2873","authenticated-orcid":false,"given":"Alec","family":"Koppel","sequence":"additional","affiliation":[{"name":"Supply Chain Optimization Technologies, Amazon, Bellevue, WA, USA"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/IEEECONF53345.2021.9723342"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989436"},{"article-title":"Intensity estimation for poisson processes","year":"2013","author":"Drazek","key":"ref3"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-9096-9"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2968813"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2014.2327238"},{"key":"ref8","doi-asserted-by":"crossref","DOI":"10.1137\/1.9781611973433","volume-title":"Lectures on Stochastic Programming: Modeling and Theory","author":"Shapiro","year":"2014"},{"key":"ref9","volume-title":"Wets, Variational Analysis","volume":"317","author":"Rockafellar","year":"2009"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-6377(02)00231-6"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(92)90012-8"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1017\/cbo9780511624216"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1214\/009053607000000677"},{"key":"ref14","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume-title":"Adv. Neural Inf. Process. Syst.","volume":"25","author":"Krizhevsky","year":"2012"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TASL.2011.2173371"},{"key":"ref16","first-page":"1399","article-title":"End-to-end kernel learning with supervised convolutional kernel networks","volume-title":"Adv. Neural Inf. Process. Syst.","volume":"29","author":"Mairal","year":"2016"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2004.830991"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.acha.2018.05.005"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/0022-247X(71)90184-3"},{"key":"ref20","first-page":"682","article-title":"Using the Nystrm method to speed up kernel machines","volume-title":"Proc. 14th Annu. Conf. Neural Inf. Process. Syst.","author":"Williams","year":"2001"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2008.2009895"},{"key":"ref22","first-page":"1177","article-title":"Random features for large-scale kernel machines","volume-title":"Adv. Neural Inf. Process. Syst.","volume":"20","author":"Rahimi","year":"2008"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1214\/16-AOS1472"},{"key":"ref24","first-page":"3103","article-title":"Breaking the curse of kernelization: Budgeted stochastic gradient descent for large-scale SVM training","volume":"13","author":"Wang","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref25","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1023\/A:1013955821559","article-title":"Kernel matching pursuit","volume":"48","author":"Vincent","year":"2002","journal-title":"Mach. Learn."},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2017.7953042"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1214\/aop\/1176996454"},{"key":"ref28","first-page":"14144","article-title":"Learning positive functions with pseudo mirror descent","volume-title":"Adv. Neural Inf. Process. Syst.","volume":"32","author":"Yang","year":"2019"},{"key":"ref29","first-page":"2627","article-title":"Convolutional kernel networks","volume-title":"Adv. Neural Inf. Process. Syst.","volume":"27","author":"Mairal","year":"2014"},{"key":"ref30","first-page":"45","article-title":"Pseudogradient adaptation and training algorithms","volume":"34","author":"Poljak","year":"1973","journal-title":"Automat. Remote Control"},{"key":"ref31","first-page":"645","article-title":"Second-order kernel online convex optimization with adaptive sketching","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Calandriello","year":"2017"},{"key":"ref32","article-title":"Efficient second-order online kernel learning with adaptive embedding","volume-title":"Adv. Neural Inf. Process. Syst.","volume":"30","author":"Calandriello","year":"2017"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1214\/17-EJS1339SI"},{"key":"ref34","first-page":"12816","article-title":"Non-parametric models for non-negative functions","volume-title":"Neural Inf. Process. Syst.","volume":"33","author":"Marteau-Ferey","year":"2020"},{"key":"ref35","first-page":"384","article-title":"Hard shape-constrained kernel machines","volume-title":"Adv. Neural Inf. Process. Syst.","volume":"33","author":"Aubin-Frankowski","year":"2020"},{"article-title":"Handling hard affine SDP shape constraints in RKHSs","year":"2021","author":"Aubin-Frankowski","key":"ref36"},{"volume-title":"Numerical Optimization","year":"2006","author":"Nocedal","key":"ref37"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.15388\/Informatica.2009.250"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2008.929943"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2012.6426626"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1137\/17M1122943"},{"key":"ref42","article-title":"Incremental greedy BFGS: An incremental quasi-Newton method with explicit superlinear rate","volume-title":"Adv. Neural Inf. Process. Syst. 12th OPT Workshop Optim. Mach. Learn.","author":"Gao","year":"2020"},{"key":"ref43","article-title":"Sparse Gaussian processes using pseudo-inputs","volume":"18","author":"Snelson","year":"2006","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2015.2388583"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1214\/14-AOS1238"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1137\/S1052623497331063"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/BF02096261"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1137\/16M1080173"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/14103.003.0007"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1137\/070704277"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/LCSYS.2018.2854889"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6420\/aa72b2"},{"issue":"1","key":"ref53","first-page":"3202","article-title":"Distributed learning with regularized least squares","volume":"18","author":"Lin","year":"2017","journal-title":"J. Mach. Learn. Res."},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/s10957-020-01805-8"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1201\/9781315140919"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2004.830985"}],"container-title":["IEEE Transactions on Signal Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/78\/9675017\/09770331.pdf?arnumber=9770331","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T23:04:01Z","timestamp":1705964641000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9770331\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":56,"URL":"https:\/\/doi.org\/10.1109\/tsp.2022.3173146","relation":{},"ISSN":["1053-587X","1941-0476"],"issn-type":[{"type":"print","value":"1053-587X"},{"type":"electronic","value":"1941-0476"}],"subject":[],"published":{"date-parts":[[2022]]}}}