{"id":1388,"date":"2021-10-10T21:04:51","date_gmt":"2021-10-10T21:04:51","guid":{"rendered":"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/?page_id=1388"},"modified":"2024-09-05T20:49:35","modified_gmt":"2024-09-05T20:49:35","slug":"mlwins-publications","status":"publish","type":"page","link":"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/mlwins-publications\/","title":{"rendered":"MLWins Publications"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"1388\" class=\"elementor elementor-1388\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-32ec3547 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"32ec3547\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column 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\n\t\t\t\t<\/nav>\n\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-610d41ff\" data-id=\"610d41ff\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5ee234a4 elementor-widget elementor-widget-text-editor\" data-id=\"5ee234a4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2>Related Publications<\/h2><p><strong>References<\/strong><\/p><p>[1]\u00a0Y. Tang, J. Zhang, and N. Li, &#8220;Distributed zero-order algorithms for nonconvex multi-agent op-timization,&#8221;\u00a0IEEE Transactions on Control of Network Systems. 8 (1) 269 to 281, 2021.<\/p><p>[2]\u00a0Malu, Mohit and Dasarathy, Gautam and Spanias, Andreas. &#8220;Bayesian Optimization in High-Dimensional Spaces: A Brief Survey.&#8221; International Conference on Information Intelligence Systems and Applications.<\/p><p>[3]\u00a0Li, Weizhi and Dasarathy, Gautam and Ramamurthy, Karthikeyan N. and Berisha, Visar. &#8220;Finding the Homology of Decision Boundaries with Active Learning.&#8221; Advances in neural information processing systems 2020.<\/p><p>[4]\u00a0J. Zhang, N. Li and Dedeoglu: &#8220;Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach,&#8221;\u00a0presented at INFOCOM 2021.<\/p><p>[5]\u00a0Z. Liu, M. Del Rosario\u2020 and Z. Ding, &#8220;A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback,&#8221;\u00a0in IEEE Transactions on Wireless Communications, doi: 10.1109\/TWC.2021.3103120.<\/p><p>[6]\u00a0Y. -C. Lin, Z. Liu, T. -S. Lee and Z. Ding, &#8220;Deep Learning Phase Compression for MIMO CSI Feedback by Exploiting FDD Channel Reciprocity,&#8221;\u00a0in IEEE Wireless Communications Letters, doi: 10.1109\/LWC.2021.3096808.<\/p><p>[7]\u00a0S. Zhang, S. Cui and Z. Ding, &#8220;Hypergraph Spectral Analysis and Processing in 3D Point Cloud,&#8221;\u00a0in IEEE Transactions on Image Processing, vol. 30, pp. 1193-1206, 2021, doi: 10.1109\/TIP.2020.3042088.<\/p><p class=\"\">[8] Mason del Rosario and <span class=\"searchHighlight\">Zhi<\/span> <span class=\"searchHighlight\">Ding<\/span>, &#8220;Learning-Based MIMO Channel Estimation under Practical Pilot Sparsity and Feedback Compression&#8221;, IEEE Transactions on Wireless Communications, Accepted, 2022.<\/p><p class=\"\">[9] Z. Liu, M. Del Rosario\u2020 and Z. <span class=\"searchHighlight\">Ding<\/span>, &#8220;A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback,&#8221; in\u00a0IEEE Transactions on Wireless Communications. doi: 10.1109\/TWC.2021.3103120.<\/p><p class=\"\">[10] Chamain, Lahiru D. and Qi, Siyu and <span class=\"searchHighlight\">Ding<\/span>, <span class=\"searchHighlight\">Zhi<\/span>, &#8220;End-to-End Image Classificationand Compression with variational autoencoders&#8221;, IEEE Internet of Things Journal, 2022. doi:\u00a0<a class=\"\" title=\"https:\/\/doi.org\/10.1109\/JIOT.2022.3182313\" href=\"https:\/\/doi.org\/10.1109\/JIOT.2022.3182313\" target=\"_blank\" rel=\"noopener\">10.1109\/JIOT.2022.3182313<\/a><\/p><p class=\"\">[11] S. Zhang, S. Cui and Z. <span class=\"searchHighlight\">Ding<\/span>, &#8220;Hypergraph Spectral Analysis and Processing in 3D Point Cloud,&#8221; in IEEE Transactions on Image Processing, vol. 30, pp. 1193-1206, 2021. doi: 10.1109\/TIP.2020.3042088.<\/p><p class=\"\">[12] Q. Deng, S. Zhang, and Z. <span class=\"searchHighlight\">Ding<\/span>, &#8220;Point Cloud Resampling via Hypergraph Signal Processing&#8221;, in\u00a0IEEE Signal Processing Letters, vol. 28, pp. 2117-2121, 2021. doi: 10.1109\/LSP.2021.3119257.<\/p><p class=\"\">[13] Y. -C. Lin, Z. Liu, T. -S. Lee and Z. <span class=\"searchHighlight\">Ding<\/span>, &#8220;Deep Learning for Partial MIMO CSI Feedback by Exploiting Channel Temporal Correlation,&#8221;\u00a055th Asilomar Conference on Signals, Systems, and Computers, 2021, doi:10.1109\/IEEECONF53345.2021.9723211.<\/p><p>[14] <a href=\"https:\/\/dblp.org\/pid\/22\/10163-2.html\"><span title=\"Yujie Tang 0002\">Yujie Tang<\/span><\/a>,\u00a0<a href=\"https:\/\/dblp.org\/pid\/274\/1008.html\"><span title=\"Vikram Ramanathan\">Vikram Ramanathan<\/span><\/a>,\u00a0<span class=\"this-person\">Junshan Zhang<\/span>,\u00a0<a href=\"https:\/\/dblp.org\/pid\/18\/3173-2.html\"><span title=\"Na Li 0002\">Na Li<\/span><\/a>: &#8220;<span class=\"title\">Communication-Efficient Distributed SGD With Compressed Sensing.&#8221;<\/span>\u00a0<a href=\"https:\/\/dblp.org\/db\/journals\/csysl\/csysl6.html#TangRZL22\">IEEE Control. Syst. Lett.\u00a06<\/a>:\u00a02054-2059\u00a0(2022)<\/p><p><em><cite class=\"data tts-content\">[15] <a href=\"https:\/\/dblp.org\/pid\/70\/9499.html\"><span title=\"Sen Lin\">Sen Lin<\/span><\/a>,\u00a0<a href=\"https:\/\/dblp.org\/pid\/09\/3925.html\"><span title=\"Li Yang\">Li Yang<\/span><\/a>,\u00a0<a href=\"https:\/\/dblp.org\/pid\/129\/1701.html\"><span title=\"Deliang Fan\">Deliang Fan<\/span><\/a>,\u00a0<span class=\"this-person\">Junshan Zhang<\/span>: <span class=\"title\">&#8220;TRGP: Trust Region Gradient Projection for Continual Learning.&#8221;<\/span>\u00a0<a href=\"https:\/\/dblp.org\/db\/conf\/iclr\/iclr2022.html#LinYFZ22\">ICLR\u00a02022<\/a><\/cite><\/em><\/p><p>[16] <cite class=\"data tts-content\"><a href=\"https:\/\/dblp.org\/pid\/21\/5494.html\"><span title=\"Hang Wang\">Hang Wang<\/span><\/a>,\u00a0<a href=\"https:\/\/dblp.org\/pid\/70\/9499.html\"><span title=\"Sen Lin\">Sen Lin<\/span><\/a>,\u00a0<span class=\"this-person\">Junshan Zhang<\/span>:&#8221;<span class=\"title\">Adaptive Ensemble Q-learning: Minimizing Estimation Bias via Error Feedback.&#8221; <\/span>\u00a0<a href=\"https:\/\/dblp.org\/db\/conf\/nips\/neurips2021.html#WangLZ21\">NeurIPS\u00a02021<\/a>: 24778-24790.<\/cite><\/p><p>[17] Xuanyu Cao and Tamer Basar and Suhas N. Diggavi and Yonina C. Eldar and Khaled B. Letaief and H. Vincent Poor and Junshan Zhang: &#8220;Communication-Efficient Distributed Learning: An Overview.&#8221; IEEE J. Sel. Areas Commun. 41(4): 851-873 (2023).<\/p><p>[18] Q. Wu, X. Chen, Z. Zhou, and J. Zhang, &#8220;FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring,\u201d\u00a0\u00a0IEEE Transactions on Mobile Computing, 21(8): 2818-2832 (2022).\u00a0<\/p><p>[19] Qiong Wu and Xu Chen and Tao Ouyang and Zhi Zhou and Xiaoxi Zhang and Shusen Yang and Junshan Zhang:<br \/>&#8220;HiFlash: Communication-Efficient Hierarchical Federated Learning With Adaptive Staleness Control and Heterogeneity-Aware Client-Edge Association.&#8221; IEEE Trans. Parallel Distributed Syst. 34(5): 1560-1579 (2023)<\/p><p>[20] Xuanyu Cao and Tamer Basar and Suhas N. Diggavi and Yonina C. Eldar and Khaled B. Letaief and H. Vincent Poor and Junshan Zhang: &#8220;Guest Editorial Communication-Efficient Distributed Learning Over Networks.&#8221; IEEE J. Sel. Areas Commun. 41(4): 845-850 (2023)<\/p><p>\u00a0<\/p><p>[21] Zhang, Songyang and Deng, Qinwen and Ding, <span class=\"outlook-search-highlight\" data-markjs=\"true\">Zhi<\/span>.\u00a0(2024).\u00a0Signal Processing Over Multilayer Graphs: Theoretical Foundations and Practical Applications.\u00a0\u00a0<em>IEEE Internet of Things Journal<\/em>. 11\u00a0 \u00a0 \u00a0\u00a0<\/p><p class=\"bottomspacing\">\u00a0<\/p><p>[22] Lin, Yu-Chien and Lee, Ta-Sung and Ding, <span class=\"outlook-search-highlight\" data-markjs=\"true\">Zhi<\/span>.\u00a0(2023).\u00a0A Scalable Deep Learning Framework for Dynamic CSI Feedback with Variable Antenna Port Numbers.\u00a0\u00a0<em>IEEE Transactions on Wireless Communications<\/em>.\u00a0 \u00a0<\/p><p>[23] Qi, Siyu and Chamain, Lahiru D. and Ding, <span class=\"outlook-search-highlight\" data-markjs=\"true\">Zhi<\/span>.\u00a0(2022).\u00a0Hierarchical Training for Distributed Deep Learning Based on Multimedia Data over Band-Limited Networks.\u00a0\u00a0<em>Proceedings International Conference on Image Processing<\/em>.\u00a0 .\u00a0<\/p><p class=\"bottomspacing\">\u00a0[24] Feres, Carlos and Levy, Bernard C. and Ding, <span class=\"outlook-search-highlight\" data-markjs=\"true\">Zhi<\/span>.\u00a0(2024).\u00a0Over-the-Air Multisensor Collaboration for Resource Efficient Joint Detection.\u00a0\u00a0<em>IEEE Transactions on Signal Processing<\/em>. 72 .\u00a0<\/p><p class=\"bottomspacing\">[25] Zhang, Songyang and Deng, Qinwen and Ding, <span class=\"outlook-search-highlight\" data-markjs=\"true\">Zhi<\/span>.\u00a0(2022).\u00a0Multilayer graph spectral analysis for hyperspectral images.\u00a0\u00a0<em>EURASIP Journal on Advances in Signal Processing<\/em>. 2022 \u00a0(1) .\u00a0<\/p><p class=\"bottomspacing\">[26] Del Rosario, Mason and Ding, <span class=\"outlook-search-highlight\" data-markjs=\"true\">Zhi<\/span>.\u00a0(2023).\u00a0Learning-Based MIMO Channel Estimation under Practical Pilot Sparsity and Feedback Compression.\u00a0\u00a0<em>IEEE transactions on wireless communications<\/em>. 22 \u00a0(2) 1161-1174.\u00a0\u00a0<\/p><p class=\"bottomspacing\">[27] Hsu, Chih-Ho and Feres, Carlos and Ding, <span class=\"outlook-search-highlight\" data-markjs=\"true\">Zhi<\/span>.\u00a0(2023).\u00a0Spectral Clustering Aided User Grouping and Scheduling in Wideband MU-MIMO Systems.\u00a0\u00a0<em>IEEE International Conference on Communications<\/em>.\u00a0<\/p><p class=\"bottomspacing\">[28] Lin, Yu-Chien and Lee, Ta-Sung and Ding, <span class=\"outlook-search-highlight\" data-markjs=\"true\">Zhi<\/span>.\u00a0(2023).\u00a0Exploiting Partial FDD Reciprocity for Beam Based Pilot Precoding and CSI Feedback in Deep Learning.\u00a0\u00a0<em>IEEE Transactions on Wireless Communications<\/em>.\u00a0 Accepted.\u00a0\u00a0<\/p><p>[29] Zhang, Songyang and Yu, Tianhang and Tivald, Jonathan and Choi, Brian and Ouyang, Feng and Ding, <span class=\"outlook-search-highlight\" data-markjs=\"true\">Zhi<\/span>.\u00a0(2022).\u00a0Exemplar-Based Radio Map Reconstruction of Missing Areas Using Propagation Priority.\u00a0\u00a0<em>GLOBECOM 2022 &#8211; 2022 IEEE Global Communications Conference<\/em>.\u00a0 1217 to 1222.\u00a0\u00a0<\/p><p class=\"bottomspacing\">\u00a0<\/p><p>[30] Deng, Qinwen and Zhang, Songyang and Ding, <span class=\"outlook-search-highlight\" data-markjs=\"true\">Zhi<\/span>.\u00a0(2021).\u00a0Point Cloud Resampling via Hypergraph Signal Processing.\u00a0\u00a0<em>IEEE Signal Processing Letters<\/em>. 28 2117 to 2121.\u00a0\u00a0<\/p><p>[31] Chamain, Lahiru D. and Qi, Siyu and Ding, <span class=\"outlook-search-highlight\" data-markjs=\"true\">Zhi<\/span>.\u00a0(2022).\u00a0End-to-End Image Classification and Compression with variational autoencoders.\u00a0\u00a0<em>IEEE Internet of Things Journal<\/em>.\u00a0 1 to 1.\u00a0\u00a0<\/p><p>[32] Lin, Yu-Chien and Lee, Ta-Sung and Ding, <span class=\"outlook-search-highlight\" data-markjs=\"true\">Zhi<\/span>.\u00a0(2021).\u00a0Deep Learning for Partial MIMO CSI Feedback by Exploiting Channel Temporal Correlation.\u00a0\u00a0<em>55th Asilomar Conference on Signals, Systems, and Computers<\/em>.\u00a0 345 to 350.\u00a0\u00a0<\/p><p>[33] Lin, Yu-Chien and Liu, Zhenyu and Lee, Ta-Sung and Ding, <span class=\"outlook-search-highlight\" data-markjs=\"true\">Zhi<\/span>.\u00a0(2021).\u00a0Deep Learning Phase Compression for MIMO CSI Feedback by Exploiting FDD Channel Reciprocity.\u00a0\u00a0<em>IEEE Wireless Communications Letters<\/em>.\u00a0 1 to 1.\u00a0\u00a0<\/p><p>[34] Liu, Zhenyu and del Rosario, Mason and Ding, <span class=\"outlook-search-highlight\" data-markjs=\"true\">Zhi<\/span>.\u00a0(2022).\u00a0A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback.\u00a0\u00a0<em>IEEE Transactions on Wireless Communications<\/em>. 21 \u00a0(2) 1214 to 1228.\u00a0\u00a0<\/p><p>[35] Zhang, Songyang and Cui, Shuguang and Ding, <span class=\"outlook-search-highlight\" data-markjs=\"true\">Zhi<\/span>.\u00a0(2021).\u00a0Hypergraph Spectral Analysis and Processing in 3D Point Cloud.\u00a0\u00a0<em>IEEE Transactions on Image Processing<\/em>. 30 1193 to 1206.\u00a0\u00a0<\/p><p>[36] Zhenyu Liu, Mason del. (2021). A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback.\u00a0 <em>IEEE transactions on wireless communications<\/em>.\u00a0<\/p><p>\u00a0<\/p><p>A Code Repositories<\/p><ul style=\"font-weight: 400\"><li>Active Learning homology\u00a0<a href=\"https:\/\/github.com\/wayne0908\/Active-Learning-Homology\">[Codebase]<\/a><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Menu MLWiNS Educational Activities Objectives Outreach and Broader Impact Outcomes Publications and Code Repositories Tasks Team Members Related Publications References [1]\u00a0Y. Tang, J. Zhang, and N. Li, &#8220;Distributed zero-order algorithms for nonconvex multi-agent op-timization,&#8221;\u00a0IEEE Transactions on Control of Network Systems. 8 (1) 269 to 281, 2021. [2]\u00a0Malu, Mohit and Dasarathy, Gautam and Spanias, Andreas. &#8220;Bayesian [&hellip;]<\/p>\n","protected":false},"author":64,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"elementor_header_footer","meta":{"inline_featured_image":false,"footnotes":""},"class_list":["post-1388","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/wp-json\/wp\/v2\/pages\/1388","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/wp-json\/wp\/v2\/users\/64"}],"replies":[{"embeddable":true,"href":"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/wp-json\/wp\/v2\/comments?post=1388"}],"version-history":[{"count":34,"href":"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/wp-json\/wp\/v2\/pages\/1388\/revisions"}],"predecessor-version":[{"id":2039,"href":"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/wp-json\/wp\/v2\/pages\/1388\/revisions\/2039"}],"wp:attachment":[{"href":"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/wp-json\/wp\/v2\/media?parent=1388"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}