{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T22:14:56Z","timestamp":1761948896867,"version":"3.44.0"},"reference-count":39,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Signal Process."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tsp.2025.3598246","type":"journal-article","created":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T18:04:22Z","timestamp":1755021862000},"page":"3270-3285","source":"Crossref","is-referenced-by-count":1,"title":["One-Bit Quantization and Sparsification for Multiclass Linear Classification With Strong Regularization"],"prefix":"10.1109","volume":"73","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3232-9359","authenticated-orcid":false,"given":"Reza","family":"Ghane","sequence":"first","affiliation":[{"name":"Electrical Engineering Department, California Institute of Technology, Pasadena, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9238-9636","authenticated-orcid":false,"given":"Danil","family":"Akhtiamov","sequence":"additional","affiliation":[{"name":"Computing + Mathematical Sciences Department, California Institute of Technology, Pasadena, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1375-5838","authenticated-orcid":false,"given":"Babak","family":"Hassibi","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, California Institute of Technology, Pasadena, CA, USA"}]}],"member":"263","reference":[{"article-title":"Massive language models can be accurately pruned in one-shot","year":"2023","author":"Frantar","key":"ref1"},{"article-title":"QMOE: Practical sub-1-bit compression of trillion-parameter models","year":"2023","author":"Frantar","key":"ref2"},{"article-title":"BitNet: Scaling 1-bit transformers for large language models","year":"2023","author":"Wang","key":"ref3"},{"article-title":"PB-LLM: Partially binarized large language models","year":"2023","author":"Shang","key":"ref4"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref6","first-page":"3104","article-title":"Sequence to sequence learning with neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Sutskever","year":"2014"},{"article-title":"Categorical Reparameterization with Gumbel-Softmax","year":"2016","author":"Jang","key":"ref7"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959190"},{"key":"ref9","first-page":"8571","article-title":"Neural tangent kernel: Convergence and generalization in neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Jacot","year":"2018"},{"key":"ref10","first-page":"15954","article-title":"On the linearity of large non-linear models: When and why the tangent kernel is constant","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Liu","year":"2020"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/ad292a"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2018.2840720"},{"issue":"1","key":"ref13","first-page":"1472","article-title":"Asymptotic behavior of support vector machine for spiked population model","volume":"18","author":"Huang","year":"2017","journal-title":"J. Mach. Learn. Res."},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1214\/18-aos1789"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1810420116"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ojsp.2021.3051849"},{"key":"ref17","first-page":"12005","article-title":"The impact of regularization on high-dimensional logistic regression","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Salehi","year":"2019"},{"key":"ref18","first-page":"3739","article-title":"Sharp asymptotics and optimal performance for inference in binary models","volume-title":"Proc. Mach. Learn. Res.","author":"Taheri","year":"2020"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1093\/imaiai\/iaab002"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1214\/25-aos2489"},{"article-title":"The role of regularization in classification of high-dimensional noisy Gaussian mixture","year":"2020","author":"Mignacco","key":"ref21"},{"article-title":"Regularization in high-dimensional regression and classification via random matrix theory","year":"2020","author":"Lolas","key":"ref22"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT57864.2024.10619631"},{"key":"ref24","first-page":"1683","article-title":"Regularized linear regression: A precise analysis of the estimation error","volume-title":"Proc. Conf. Learn. Theory","author":"Thrampoulidis","year":"2015"},{"article-title":"A framework to characterize performance of lasso algorithms","year":"2013","author":"Stojnic","key":"ref25"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/BF02759761"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/tit.2023.3320098"},{"key":"ref28","first-page":"23479","article-title":"Generalization for multiclass classification with overparameterized linear models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Subramanian","year":"2022"},{"key":"ref29","first-page":"10144","article-title":"Learning gaussian mixtures with generalized linear models: Precise asymptotics in high-dimensions","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Loureiro","year":"2021"},{"key":"ref30","first-page":"8907","article-title":"Theoretical insights into multiclass classification: A high-dimensional asymptotic view","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Thrampoulidis","year":"2020"},{"key":"ref31","first-page":"3381","article-title":"A universal analysis of large-scale regularized least squares solutions","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Panahi","year":"2017"},{"key":"ref32","first-page":"4310","article-title":"Universality of empirical risk minimization","volume-title":"Proc. Conf. Learn. Theory","author":"Montanari","year":"2022"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/ad65e7"},{"article-title":"Universality in transfer learning for linear models","year":"2024","author":"Ghane","key":"ref34"},{"issue":"83","key":"ref35","first-page":"1","article-title":"CVXPY: A Python-embedded modeling language for convex optimization","volume":"17","author":"Diamond","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1080\/23307706.2017.1397554"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-46092-0"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1038\/sj.jors.2600425"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2018.2831622"}],"container-title":["IEEE Transactions on Signal Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/78\/10807692\/11123530.pdf?arnumber=11123530","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T05:59:18Z","timestamp":1757483958000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11123530\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":39,"URL":"https:\/\/doi.org\/10.1109\/tsp.2025.3598246","relation":{},"ISSN":["1053-587X","1941-0476"],"issn-type":[{"type":"print","value":"1053-587X"},{"type":"electronic","value":"1941-0476"}],"subject":[],"published":{"date-parts":[[2025]]}}}