{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T13:32:52Z","timestamp":1772544772866,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":35,"publisher":"ACM","license":[{"start":{"date-parts":[[2017,8,4]],"date-time":"2017-08-04T00:00:00Z","timestamp":1501804800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["CCF-1564000"],"award-info":[{"award-number":["CCF-1564000"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["CCF-1320746"],"award-info":[{"award-number":["CCF-1320746"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["IIS-1546452"],"award-info":[{"award-number":["IIS-1546452"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2017,8,4]]},"DOI":"10.1145\/3097983.3098080","type":"proceedings-article","created":{"date-parts":[[2017,8,4]],"date-time":"2017-08-04T18:35:54Z","timestamp":1501871754000},"page":"245-254","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Communication-Efficient Distributed Block Minimization for Nonlinear Kernel Machines"],"prefix":"10.1145","author":[{"given":"Cho-Jui","family":"Hsieh","sequence":"first","affiliation":[{"name":"University of California, Davis, Davis, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Si","family":"Si","sequence":"additional","affiliation":[{"name":"Google Inc. & Google Research, Mountain View, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Inderjit S.","family":"Dhillon","sequence":"additional","affiliation":[{"name":"University of Texas at Austin, Austin, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2017,8,4]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.1206"},{"key":"e_1_3_2_2_2_1","volume-title":"echnical Report. Department of Computer Science and Information Engineering","author":"Chang Chih-Chung","unstructured":"Chih-Chung Chang and Chih-Jen Lin 2000. LIBSVM: Introduction and Benchmarks. bibinfotype T echnical Report. Department of Computer Science and Information Engineering , National Taiwan University , Taipei, Taiwan . Chih-Chung Chang and Chih-Jen Lin 2000. LIBSVM: Introduction and Benchmarks. bibinfotypeTechnical Report. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan."},{"key":"e_1_3_2_2_3_1","unstructured":"E. Chang K. Zhu H. Wang H. Bai J. Li Z. Qiu and H. Cui. 2008. Parallelizing Support Vector Machines on Distributed Computers. NIPS.  E. Chang K. Zhu H. Wang H. Bai J. Li Z. Qiu and H. Cui. 2008. Parallelizing Support Vector Machines on Distributed Computers. NIPS."},{"key":"e_1_3_2_2_4_1","unstructured":"H. P. Graf E. Cosatto L. Bottou I. Dundanovic and V. Vapnik 2005. Parallel Support Vector Machines: The Cascade SVM NIPS.  H. P. Graf E. Cosatto L. Bottou I. Dundanovic and V. Vapnik 2005. Parallel Support Vector Machines: The Cascade SVM NIPS."},{"key":"e_1_3_2_2_5_1","unstructured":"C. J. Hsieh S. Si and I. S. Dhillon 2014. A Divide-and-Conquer Solver for Kernel Support Vector Machines ICML.  C. J. Hsieh S. Si and I. S. Dhillon 2014. A Divide-and-Conquer Solver for Kernel Support Vector Machines ICML."},{"key":"e_1_3_2_2_6_1","unstructured":"Cho-Jui Hsieh Si Si and Inderjit S Dhillon. 2014. Fast prediction for large-scale kernel machines. Advances in Neural Information Processing Systems. 3689--3697.  Cho-Jui Hsieh Si Si and Inderjit S Dhillon. 2014. Fast prediction for large-scale kernel machines. Advances in Neural Information Processing Systems. 3689--3697."},{"key":"e_1_3_2_2_7_1","unstructured":"C.-J. Hsieh H. F. Yu and I. S. Dhillon 2015. PASSCoDe: Parallel ASynchronous Stochastic dual Coordinate Descent ICML.  C.-J. Hsieh H. F. Yu and I. S. Dhillon 2015. PASSCoDe: Parallel ASynchronous Stochastic dual Coordinate Descent ICML."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"crossref","unstructured":"P.-S. Huang H. Avron T. Sainath V. Sindhwani and B. Ramabhadran 2014. Kernel Methods Match Deep Neural Networks on TIMIT ICASSP. 205--209.  P.-S. Huang H. Avron T. Sainath V. Sindhwani and B. Ramabhadran 2014. Kernel Methods Match Deep Neural Networks on TIMIT ICASSP. 205--209.","DOI":"10.1109\/ICASSP.2014.6853587"},{"key":"e_1_3_2_2_9_1","unstructured":"M. Jaggi V. Smith M. Tak\u00e1vc J. Terhorst T. Hofmann and M. Jordan. 2014. Communication-efficient Distributed Dual Coordinate ascent. NIPS.  M. Jaggi V. Smith M. Tak\u00e1vc J. Terhorst T. Hofmann and M. Jordan. 2014. Communication-efficient Distributed Dual Coordinate ascent. NIPS."},{"key":"e_1_3_2_2_10_1","unstructured":"Thorsten Joachims. 1998. Making Large-scale SVM Learning Practical. In Advances in Kernel Methods -- Support Vector Learning.  Thorsten Joachims. 1998. Making Large-scale SVM Learning Practical. In Advances in Kernel Methods -- Support Vector Learning."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-005-0768-5"},{"key":"e_1_3_2_2_12_1","unstructured":"S. Kumar M. Mohri and A. Talwalkar 2009. Ensemble Nystr\u00f6m Method. In NIPS.  S. Kumar M. Mohri and A. Talwalkar 2009. Ensemble Nystr\u00f6m Method. In NIPS."},{"key":"e_1_3_2_2_13_1","unstructured":"Q. Le T. Sarl\u00f3s and A. Smola 2013. Fastfood -- Approximating Kernel Expansions in Loglinear Time ICML.  Q. Le T. Sarl\u00f3s and A. Smola 2013. Fastfood -- Approximating Kernel Expansions in Loglinear Time ICML."},{"key":"e_1_3_2_2_14_1","unstructured":"C.-P. Lee and D. Roth. 2015. Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM ICML.  C.-P. Lee and D. Roth. 2015. Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM ICML."},{"key":"e_1_3_2_2_15_1","unstructured":"Chenxin Ma Virginia Smith Martin Jaggi Michael I. Jordan Peter Richt\u00e1rik and Martin Tak\u00e1vc 2015. Adding vs. Averaging in Distributed Primal-Dual Optimization ICML.  Chenxin Ma Virginia Smith Martin Jaggi Michael I. Jordan Peter Richt\u00e1rik and Martin Tak\u00e1vc 2015. Adding vs. Averaging in Distributed Primal-Dual Optimization ICML."},{"key":"e_1_3_2_2_16_1","volume-title":"A Distributed Algorithm for Training Nonlinear Kernel Machines. arXiv:1405.4543","author":"Mahajan Dhruv","year":"2014","unstructured":"Dhruv Mahajan , S. Sathiya Keerthi , and Sundararajan Sellamanickam . 2014. A Distributed Algorithm for Training Nonlinear Kernel Machines. arXiv:1405.4543 ( 2014 ). Dhruv Mahajan, S. Sathiya Keerthi, and Sundararajan Sellamanickam. 2014. A Distributed Algorithm for Training Nonlinear Kernel Machines. arXiv:1405.4543 (2014)."},{"key":"e_1_3_2_2_17_1","volume-title":"A distributed block coordinate descent method for training L1 regularized linear classifiers. arXiv:1405.4544","author":"Mahajan Dhruv","year":"2014","unstructured":"Dhruv Mahajan , S. Sathiya Keerthi , and Sundararajan Sellamanickam . 2014. A distributed block coordinate descent method for training L1 regularized linear classifiers. arXiv:1405.4544 ( 2014 ). Dhruv Mahajan, S. Sathiya Keerthi, and Sundararajan Sellamanickam. 2014. A distributed block coordinate descent method for training L1 regularized linear classifiers. arXiv:1405.4544 (2014)."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"crossref","unstructured":"John C. Platt. 1998. Fast Training of Support Vector Machines using Sequential Minimal Optimization Advances in Kernel Methods - Support Vector Learning.  John C. Platt. 1998. Fast Training of Support Vector Machines using Sequential Minimal Optimization Advances in Kernel Methods - Support Vector Learning.","DOI":"10.7551\/mitpress\/1130.003.0016"},{"key":"e_1_3_2_2_19_1","unstructured":"Ali Rahimi and Benjamin Recht 2008. Random Features for Large-scale Kernel Machines. NIPS.  Ali Rahimi and Benjamin Recht 2008. Random Features for Large-scale Kernel Machines. NIPS."},{"key":"e_1_3_2_2_20_1","volume-title":"Parallel Coordinate Descent Methods for Big Data Optimization. Mathematical Programming","author":"Richt\u00e1rik P.","year":"2012","unstructured":"P. Richt\u00e1rik and M. Tak\u00e1vc 2012. Parallel Coordinate Descent Methods for Big Data Optimization. Mathematical Programming ( 2012 ). P. Richt\u00e1rik and M. Tak\u00e1vc 2012. Parallel Coordinate Descent Methods for Big Data Optimization. Mathematical Programming (2012)."},{"key":"e_1_3_2_2_21_1","unstructured":"C. Scherrer A. Tewari M. Halappanavar and D. Haglin. 2012. Feature Clustering for Accelerating Parallel Coordinate Descent NIPS.  C. Scherrer A. Tewari M. Halappanavar and D. Haglin. 2012. Feature Clustering for Accelerating Parallel Coordinate Descent NIPS."},{"key":"e_1_3_2_2_22_1","volume-title":"Dhillon","author":"Si Si","year":"2016","unstructured":"Si Si , Kai-Yang Chiang , Cho-Jui Hsieh , Nikhil Rao , and Inderjit S . Dhillon 2016 . Goal-Directed Inductive Matrix Completion. In KDD. 1165--1174. Si Si, Kai-Yang Chiang, Cho-Jui Hsieh, Nikhil Rao, and Inderjit S. Dhillon 2016. Goal-Directed Inductive Matrix Completion. In KDD. 1165--1174."},{"key":"e_1_3_2_2_23_1","volume-title":"Dhillon","author":"Si Si","year":"2016","unstructured":"Si Si , Cho-Jui Hsieh , and Inderjit S . Dhillon . 2016 . Computationally Efficient Nystr\u00f6m Approximation using Fast Transforms ICML. 2655--2663. Si Si, Cho-Jui Hsieh, and Inderjit S. Dhillon. 2016. Computationally Efficient Nystr\u00f6m Approximation using Fast Transforms ICML. 2655--2663."},{"key":"e_1_3_2_2_24_1","unstructured":"S. Si C. J. Hsieh and I. S. Dhillon 2017. Memory Efficient Kernel Approximation. (2017).  S. Si C. J. Hsieh and I. S. Dhillon 2017. Memory Efficient Kernel Approximation. (2017)."},{"key":"e_1_3_2_2_25_1","volume-title":"Large Scale Kernel Learning using Block Coordinate Descent. CoRR","author":"Tu Stephen","year":"2016","unstructured":"Stephen Tu , Rebecca Roelofs , Shivaram Venkataraman , and Benjamin Recht 2016. Large Scale Kernel Learning using Block Coordinate Descent. CoRR Vol. abs\/ 1602 .05310 ( 2016 ). Stephen Tu, Rebecca Roelofs, Shivaram Venkataraman, and Benjamin Recht 2016. Large Scale Kernel Learning using Block Coordinate Descent. CoRR Vol. abs\/1602.05310 (2016)."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2638584"},{"key":"e_1_3_2_2_27_1","unstructured":"C. K. I. Williams and M. Seeger 2001. Using the Nystr\u00f6m Method to Speed Up Kernel Machines NIPS.  C. K. I. Williams and M. Seeger 2001. Using the Nystr\u00f6m Method to Speed Up Kernel Machines NIPS."},{"key":"e_1_3_2_2_28_1","unstructured":"T. Yang 2013. Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent NIPS.  T. Yang 2013. Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent NIPS."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"crossref","unstructured":"Y. You J. Demmel K. Czechowski L. Song and R. Vuduc 2015. CA-SVM: Communication-Avoiding Support Vector Machines on Clusters IPDPS.  Y. You J. Demmel K. Czechowski L. Song and R. Vuduc 2015. CA-SVM: Communication-Avoiding Support Vector Machines on Clusters IPDPS.","DOI":"10.1109\/IPDPS.2015.117"},{"key":"e_1_3_2_2_30_1","unstructured":"Yang You Xiangru Lian Ji Liu Hsiang-Fu Yu Inderjit Dhillon James Demmel and Cho-Jui Hsieh 2016. Asynchronous Parallel Greedy Coordinate Descent. NIPS.  Yang You Xiangru Lian Ji Liu Hsiang-Fu Yu Inderjit Dhillon James Demmel and Cho-Jui Hsieh 2016. Asynchronous Parallel Greedy Coordinate Descent. NIPS."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-010-5221-8"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"crossref","unstructured":"Huan Zhang and Cho-Jui Hsieh 2016. Fixing the convergence problems in parallel asynchronous dual coordinate descent ICDM.  Huan Zhang and Cho-Jui Hsieh 2016. Fixing the convergence problems in parallel asynchronous dual coordinate descent ICDM.","DOI":"10.1109\/ICDM.2016.0073"},{"key":"e_1_3_2_2_33_1","volume-title":"Limited Resources: A Low-rank Linearization Approach AISTATS.","author":"Zhang K.","year":"2012","unstructured":"K. Zhang , L. Lan , Z. Wang , and F. Moerchen . 2012 . Scaling up Kernel SVM on Limited Resources: A Low-rank Linearization Approach AISTATS. K. Zhang, L. Lan, Z. Wang, and F. Moerchen. 2012. Scaling up Kernel SVM on Limited Resources: A Low-rank Linearization Approach AISTATS."},{"key":"e_1_3_2_2_34_1","unstructured":"Y. Zhang and L. Xiao. 2015. DiSCO: Communication-Efficient Distributed Optimization of Self-Concordant Loss ICML.  Y. Zhang and L. Xiao. 2015. DiSCO: Communication-Efficient Distributed Optimization of Self-Concordant Loss ICML."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"crossref","unstructured":"Zeyuan A. Zhu Weizhu Chen Gang Wang Chenguang Zhu and Zheng Chen 2009. P-packSVM: Parallel Primal Gradient Descent Kernel SVM ICDM.  Zeyuan A. Zhu Weizhu Chen Gang Wang Chenguang Zhu and Zheng Chen 2009. P-packSVM: Parallel Primal Gradient Descent Kernel SVM ICDM.","DOI":"10.1109\/ICDM.2009.29"}],"event":{"name":"KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","location":"Halifax NS Canada","acronym":"KDD '17","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3097983.3098080","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3097983.3098080","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3097983.3098080","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T03:30:26Z","timestamp":1750217426000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3097983.3098080"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,8,4]]},"references-count":35,"alternative-id":["10.1145\/3097983.3098080","10.1145\/3097983"],"URL":"https:\/\/doi.org\/10.1145\/3097983.3098080","relation":{},"subject":[],"published":{"date-parts":[[2017,8,4]]},"assertion":[{"value":"2017-08-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}