{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T22:31:57Z","timestamp":1777501917856,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":34,"publisher":"ACM","license":[{"start":{"date-parts":[[2014,8,24]],"date-time":"2014-08-24T00:00:00Z","timestamp":1408838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2014,8,24]]},"DOI":"10.1145\/2623330.2623759","type":"proceedings-article","created":{"date-parts":[[2014,8,22]],"date-time":"2014-08-22T19:38:46Z","timestamp":1408736326000},"page":"472-481","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":45,"title":["Active learning for sparse bayesian multilabel classification"],"prefix":"10.1145","author":[{"given":"Deepak","family":"Vasisht","sequence":"first","affiliation":[{"name":"MIT, Cambridge, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Damianou","sequence":"additional","affiliation":[{"name":"University of Sheffield, UK, Sheffield, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manik","family":"Varma","sequence":"additional","affiliation":[{"name":"Microsoft Research, Bangalore, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ashish","family":"Kapoor","sequence":"additional","affiliation":[{"name":"Microsoft Research, Redmond, WA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2014,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"Mulan Multilabel Datasets. http:\/\/mulan.sourceforge.net\/datasets.html.  Mulan Multilabel Datasets. http:\/\/mulan.sourceforge.net\/datasets.html."},{"key":"e_1_3_2_2_2_1","volume-title":"WWW","author":"Agrawal R.","year":"2013","unstructured":"R. Agrawal , A. Gupta , Y. Prabhu , and M. Varma . Multi-label learning with millions of labels: Recommending advertiser bid phrases for web pages . In WWW , 2013 . R. Agrawal, A. Gupta, Y. Prabhu, and M. Varma. Multi-label learning with millions of labels: Recommending advertiser bid phrases for web pages. In WWW, 2013."},{"key":"e_1_3_2_2_3_1","volume-title":"ICML","author":"Balasubramanian K.","year":"2012","unstructured":"K. Balasubramanian and G. Lebanon . The Landmark Selection Method for Multiple Output Prediction . In ICML , 2012 . K. Balasubramanian and G. Lebanon. The Landmark Selection Method for Multiple Output Prediction. In ICML, 2012."},{"key":"e_1_3_2_2_4_1","volume-title":"NIPS","author":"Bergamo A.","year":"2011","unstructured":"A. Bergamo , L. Torresani , and A. W. Fitzgibbon . PiCoDes: Learning a Compact Code for Novel-Category Recognition . In NIPS , 2011 . A. Bergamo, L. Torresani, and A. W. Fitzgibbon. PiCoDes: Learning a Compact Code for Novel-Category Recognition. In NIPS, 2011."},{"key":"e_1_3_2_2_5_1","first-page":"405","volume-title":"ICML","author":"Bi W.","year":"2013","unstructured":"W. Bi and J. T.-Y. Kwok . Efficient Multi-label Classification with Many Labels . In ICML , pages 405 -- 413 , 2013 . W. Bi and J. T.-Y. Kwok. Efficient Multi-label Classification with Many Labels. In ICML, pages 405--413, 2013."},{"key":"e_1_3_2_2_6_1","volume-title":"UAI","author":"Bishop C. M.","year":"2000","unstructured":"C. M. Bishop and M. E. Tipping . Variational Relevance Vector Machines . In UAI , 2000 . C. M. Bishop and M. E. Tipping. Variational Relevance Vector Machines. In UAI, 2000."},{"key":"e_1_3_2_2_7_1","author":"Caselton W.","year":"1984","unstructured":"W. Caselton and J. Zidek . Optimal monitoring network designs. Statistics and Probability Letters , 1984 . W. Caselton and J. Zidek. Optimal monitoring network designs. Statistics and Probability Letters, 1984.","journal-title":"Optimal monitoring network designs. Statistics and Probability Letters"},{"key":"e_1_3_2_2_8_1","first-page":"1538","volume-title":"NIPS","author":"Chen Y.-N.","year":"2012","unstructured":"Y.-N. Chen and H.-T. Lin . Feature-aware Label Space Dimension Reduction for Multi-label Classification . In NIPS , pages 1538 -- 1546 , 2012 . Y.-N. Chen and H.-T. Lin. Feature-aware Label Space Dimension Reduction for Multi-label Classification. In NIPS, pages 1538--1546, 2012."},{"key":"e_1_3_2_2_9_1","volume-title":"NIPS","author":"Chu W.","year":"2006","unstructured":"W. Chu , V. Sindhwani , Z. Ghahramani , and S. Keerthi . Relational Learning with Gaussian Processes . In NIPS , 2006 . W. Chu, V. Sindhwani, Z. Ghahramani, and S. Keerthi. Relational Learning with Gaussian Processes. In NIPS, 2006."},{"key":"e_1_3_2_2_10_1","first-page":"1851","volume-title":"NIPS","author":"Ciss\u00e9 M.","year":"2013","unstructured":"M. Ciss\u00e9 , N. Usunier , T. Arti'eres , and P. Gallinari . Robust Bloom Filters for Large MultiLabel Classification Tasks . In NIPS , pages 1851 -- 1859 , 2013 . M. Ciss\u00e9, N. Usunier, T. Arti'eres, and P. Gallinari. Robust Bloom Filters for Large MultiLabel Classification Tasks. In NIPS, pages 1851--1859, 2013."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-00958-7_12"},{"key":"e_1_3_2_2_12_1","first-page":"289","volume-title":"JMLR","author":"Feng C.-S.","year":"2011","unstructured":"C.-S. Feng and H.-T. Lin . Multi-label Classification with Error-Correcting Codes . JMLR , pages 289 -- 295 , 2011 . C.-S. Feng and H.-T. Lin. Multi-label Classification with Error-Correcting Codes. JMLR, pages 289--295, 2011."},{"key":"e_1_3_2_2_13_1","volume-title":"AAAI","author":"Goldberg A.","year":"2011","unstructured":"A. Goldberg , X. Zhu , A. Furger , and J. Xu . OASIS: Online Active Semi-Supervised Learning . In AAAI , 2011 . A. Goldberg, X. Zhu, A. Furger, and J. Xu. OASIS: Online Active Semi-Supervised Learning. In AAAI, 2011."},{"key":"e_1_3_2_2_14_1","volume-title":"JMLR","author":"Gretton A.","year":"2005","unstructured":"A. Gretton , R. Herbrich , and A. Hyv\u00e4rinen . Kernel methods for measuring independence . JMLR , 2005 . A. Gretton, R. Herbrich, and A. Hyv\u00e4rinen. Kernel methods for measuring independence. JMLR, 2005."},{"key":"e_1_3_2_2_15_1","volume-title":"NIPS","author":"Hsu D.","year":"2009","unstructured":"D. Hsu , S. Kakade , J. Langford , and T. Zhang . Multi-Label Prediction via Compressed Sensing . In NIPS , 2009 . D. Hsu, S. Kakade, J. Langford, and T. Zhang. Multi-Label Prediction via Compressed Sensing. In NIPS, 2009."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401939"},{"key":"e_1_3_2_2_17_1","volume-title":"NIPS","author":"Kapoor A.","year":"2012","unstructured":"A. Kapoor , R. Viswanathan , and P. Jain . Multilabel Classification using Bayesian Compressed Sensing . In NIPS , 2012 . A. Kapoor, R. Viswanathan, and P. Jain. Multilabel Classification using Bayesian Compressed Sensing. In NIPS, 2012."},{"key":"e_1_3_2_2_18_1","volume-title":"UAI","author":"Krause A.","year":"2005","unstructured":"A. Krause and C. Guestrin . Near-optimal Nonmyopic Value of Information in Graphical Models . In UAI , 2005 . A. Krause and C. Guestrin. Near-optimal Nonmyopic Value of Information in Graphical Models. In UAI, 2005."},{"key":"e_1_3_2_2_19_1","volume-title":"Efficient Algorithms and Empirical Studies. JMLR","author":"Krause A.","year":"2008","unstructured":"A. Krause , A. Singh , and C. Guestrin . Near-Optimal Sensor Placements in Gaussian Processes: Theory , Efficient Algorithms and Empirical Studies. JMLR , 2008 . A. Krause, A. Singh, and C. Guestrin. Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies. JMLR, 2008."},{"key":"e_1_3_2_2_20_1","volume-title":"IJCAI","author":"Li X.","year":"2013","unstructured":"X. Li and Y. Guo . Active Learning with Multi-label SVM Classification . In IJCAI , 2013 . X. Li and Y. Guo. Active Learning with Multi-label SVM Classification. In IJCAI, 2013."},{"key":"e_1_3_2_2_21_1","volume-title":"ICIP","author":"Li X.","year":"2004","unstructured":"X. Li , L. Wang , and E. Sung . Multi-label SVM Active Learning for Image Classification . In ICIP , 2004 . X. Li, L. Wang, and E. Sung. Multi-label SVM Active Learning for Image Classification. In ICIP, 2004."},{"key":"e_1_3_2_2_22_1","volume-title":"Mathematical Programming","author":"Nemhauser G.","year":"1978","unstructured":"G. Nemhauser , L. Wolsey , and M. Fisher . An analysis of approximations for maximizing submodular set functions . Mathematical Programming , 1978 . G. Nemhauser, L. Wolsey, and M. Fisher. An analysis of approximations for maximizing submodular set functions. Mathematical Programming, 1978."},{"key":"e_1_3_2_2_23_1","volume-title":"Advances In Large Margin Classifiers","author":"Platt J. C.","year":"1999","unstructured":"J. C. Platt . Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods . In Advances In Large Margin Classifiers . MIT Press , 1999 . J. C. Platt. Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods. In Advances In Large Margin Classifiers. MIT Press, 1999."},{"key":"e_1_3_2_2_24_1","volume-title":"Active learning literature survey. Technical report","author":"Settles B.","year":"2010","unstructured":"B. Settles . Active learning literature survey. Technical report , 2010 . B. Settles. Active learning literature survey. Technical report, 2010."},{"key":"e_1_3_2_2_25_1","volume-title":"Bayesian Compressive Sensing","author":"Xue Y.","year":"2007","unstructured":"Shihao, Y. Xue , and L. Carin . Bayesian Compressive Sensing , 2007 . Shihao, Y. Xue, and L. Carin. Bayesian Compressive Sensing, 2007."},{"key":"e_1_3_2_2_26_1","unstructured":"A. Singh A. Krause C. Guestrin and W. J. Kaiser. Efficient Informative Sensing using Multiple Robots.  A. Singh A. Krause C. Guestrin and W. J. Kaiser. Efficient Informative Sensing using Multiple Robots."},{"key":"e_1_3_2_2_27_1","volume-title":"Active learning for multi-label image annotation. Technical report","author":"Singh M.","year":"2009","unstructured":"M. Singh , E. Curran , and P. Cunningham . Active learning for multi-label image annotation. Technical report , University College Dublin , 2009 . M. Singh, E. Curran, and P. Cunningham. Active learning for multi-label image annotation. Technical report, University College Dublin, 2009."},{"key":"e_1_3_2_2_28_1","volume-title":"Lin. Multi-label Classification with Principal Label Space Transformation. In Workshop proceedings of learning from multi-label data","author":"Tai F.","year":"2010","unstructured":"F. Tai and H.- T. Lin. Multi-label Classification with Principal Label Space Transformation. In Workshop proceedings of learning from multi-label data , 2010 . F. Tai and H.-T. Lin. Multi-label Classification with Principal Label Space Transformation. In Workshop proceedings of learning from multi-label data, 2010."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.5555\/2283696.2283856"},{"key":"e_1_3_2_2_30_1","volume-title":"ICML","author":"Weston J.","year":"2013","unstructured":"J. Weston , A. Makadia , and H. Yee . Label Partitioning for Sublinear Ranking . In ICML , 2013 . J. Weston, A. Makadia, and H. Yee. Label Partitioning for Sublinear Ranking. In ICML, 2013."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557119"},{"key":"e_1_3_2_2_32_1","volume-title":"ICML","author":"Yu H.-F.","year":"2014","unstructured":"H.-F. Yu , P. Jain , and I. S. Dhillon . Large-scale Multi-label Learning with Missing Labels . ICML , 2014 . H.-F. Yu, P. Jain, and I. S. Dhillon. Large-scale Multi-label Learning with Missing Labels. ICML, 2014."},{"key":"e_1_3_2_2_33_1","first-page":"873","volume-title":"AISTATS","author":"Zhang Y.","year":"2011","unstructured":"Y. Zhang and J. G. Schneider . Multi-Label Output Codes using Canonical Correlation Analysis . In AISTATS , pages 873 -- 882 , 2011 . Y. Zhang and J. G. Schneider. Multi-Label Output Codes using Canonical Correlation Analysis. In AISTATS, pages 873--882, 2011."},{"key":"e_1_3_2_2_34_1","volume-title":"School of CS","author":"Zhu X.","year":"2003","unstructured":"X. Zhu , J. Lafferty , and Z. Ghahramani . Semi-Supervised Learning: From Gaussian Fields to Gaussian Processes. Technical report , School of CS , Carnegie Mellon University , 2003 . X. Zhu, J. Lafferty, and Z. Ghahramani. Semi-Supervised Learning: From Gaussian Fields to Gaussian Processes. Technical report, School of CS, Carnegie Mellon University, 2003."}],"event":{"name":"KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","location":"New York New York USA","acronym":"KDD '14","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 20th ACM SIGKDD international conference on Knowledge discovery and data mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2623330.2623759","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2623330.2623759","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T07:19:41Z","timestamp":1750231181000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2623330.2623759"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,8,24]]},"references-count":34,"alternative-id":["10.1145\/2623330.2623759","10.1145\/2623330"],"URL":"https:\/\/doi.org\/10.1145\/2623330.2623759","relation":{},"subject":[],"published":{"date-parts":[[2014,8,24]]},"assertion":[{"value":"2014-08-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}