{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T13:10:03Z","timestamp":1748783403520,"version":"3.41.0"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2016,2,19]],"date-time":"2016-02-19T00:00:00Z","timestamp":1455840000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2016,12]]},"DOI":"10.1007\/s10115-016-0926-z","type":"journal-article","created":{"date-parts":[[2016,2,19]],"date-time":"2016-02-19T15:50:34Z","timestamp":1455897034000},"page":"933-973","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A new transfer learning framework with application to model-agnostic multi-task learning"],"prefix":"10.1007","volume":"49","author":[{"given":"Sunil","family":"Gupta","sequence":"first","affiliation":[]},{"given":"Santu","family":"Rana","sequence":"additional","affiliation":[]},{"given":"Budhaditya","family":"Saha","sequence":"additional","affiliation":[]},{"given":"Dinh","family":"Phung","sequence":"additional","affiliation":[]},{"given":"Svetha","family":"Venkatesh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,2,19]]},"reference":[{"key":"926_CR1","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-70992-5","volume-title":"A general survey of privacy-preserving data mining models and algorithms","author":"CC Aggarwal","year":"2008","unstructured":"Aggarwal CC, Yu PS (2008) A general survey of privacy-preserving data mining models and algorithms. Springer, Berlin"},{"issue":"3","key":"926_CR2","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/s10994-007-5040-8","volume":"73","author":"A Argyriou","year":"2008","unstructured":"Argyriou A, Evgeniou T, Pontil M (2008) Convex multi-task feature learning. Mach Learn 73(3):243\u2013272","journal-title":"Mach Learn"},{"key":"926_CR3","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1613\/jair.731","volume":"12","author":"J Baxter","year":"2000","unstructured":"Baxter J (2000) A model of inductive bias learning. J Artif Intell Res (JAIR) 12:149\u2013198","journal-title":"J Artif Intell Res (JAIR)"},{"key":"926_CR4","doi-asserted-by":"crossref","unstructured":"Ben-David S, Schuller R (2003) Exploiting task relatedness for multiple task learning. pp 567\u2013580","DOI":"10.1007\/978-3-540-45167-9_41"},{"key":"926_CR5","doi-asserted-by":"crossref","unstructured":"Bickel S, Br\u00fcckner M, Scheffer T (2007) Discriminative learning for differing training and test distributions. In: Proceedings of the 24th international conference on machine learning, ACM, pp 81\u201388","DOI":"10.1145\/1273496.1273507"},{"key":"926_CR6","unstructured":"Bonilla EV, Chai KM, Williams C (2007) Multi-task Gaussian process prediction. In: Advances in neural information processing systems, pp 153\u2013160"},{"key":"926_CR7","unstructured":"Bonilla EV, Agakov FV, Williams C (2007) Kernel multi-task learning using task-specific features. In: International conference on artificial intelligence and statistics, pp 43\u201350"},{"key":"926_CR8","unstructured":"Bonilla EV, Kian CMA, Williams CKI (2007) Multi-task gaussian process prediction. In: Nips, vol\u00a020, pp 153\u2013160"},{"issue":"1","key":"926_CR9","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"issue":"1","key":"926_CR10","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1023\/A:1007379606734","volume":"28","author":"R Caruana","year":"1997","unstructured":"Caruana R (1997) Multitask learning. Mach Learn 28(1):41\u201375","journal-title":"Mach Learn"},{"key":"926_CR11","unstructured":"Chen M, Weinberger KQ, Blitzer J (2011) Co-training for domain adaptation. In: NIPS, pp 2456\u20132464"},{"key":"926_CR12","doi-asserted-by":"crossref","unstructured":"Clifton C, Kantarcio\u01e7lu M, Doan A, Schadow G, Vaidya J, Elmagarmid A, Suciu D (2004) Privacy-preserving data integration and sharing. In: Proceedings of the 9th ACM SIGMOD workshop on research issues in data mining and knowledge discovery, ACM, pp 19\u201326","DOI":"10.1145\/1008694.1008698"},{"key":"926_CR13","unstructured":"Dai W, Xue G-R, Yang Q, Yu Y (2007) Transferring naive bayes classifiers for text classification. In: Proceedings of the twenty-second AAAI conference on artificial intelligence, vol\u00a022, AAAI Press, p 540"},{"key":"926_CR14","doi-asserted-by":"crossref","unstructured":"Dai W, Yang Q, Xue G-R, Yu Y (2007) Boosting for transfer learning. In: Proceedings of the 24th international conference on machine learning, ACM, pp 193\u2013200","DOI":"10.1145\/1273496.1273521"},{"key":"926_CR15","unstructured":"Daum\u00e9\u00a0III H (2009) Bayesian multitask learning with latent hierarchies. In: Processing of the 25th conference on uncertainty in artificial intelligence, pp 135\u2013142"},{"key":"926_CR16","doi-asserted-by":"crossref","unstructured":"Daume\u00a0III H, Marcu D (2006) Domain adaptation for statistical classifiers. J Artif Intell Res, pp 101\u2013126","DOI":"10.1613\/jair.1872"},{"key":"926_CR17","doi-asserted-by":"crossref","unstructured":"Davis J, Domingos P (2009) Deep transfer via second-order markov logic. In: Proceedings of the 26th annual international conference on machine learning, ACM, pp 217\u2013224","DOI":"10.1145\/1553374.1553402"},{"key":"926_CR18","unstructured":"Evgeniou A, Pontil M (2007) Multi-task feature learning. In: Advances in neural information processing systems, vol\u00a019, The MIT Press, p 41"},{"key":"926_CR19","unstructured":"Evgeniou T, Micchelli CA, Pontil M (2005) Learning multiple tasks with kernel methods. J Mach Learn Res, 615\u2013637"},{"key":"926_CR20","doi-asserted-by":"crossref","unstructured":"Evgeniou T, Pontil M (2004) Regularized multi\u2013task learning. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 109\u2013117","DOI":"10.1145\/1014052.1014067"},{"issue":"5","key":"926_CR21","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1109\/TKDE.2007.1015","volume":"19","author":"BCM Fung","year":"2007","unstructured":"Fung BCM, Wang K, Yu PS (2007) Anonymizing classification data for privacy preservation. Knowl Data Eng IEEE Trans 19(5):711\u2013725","journal-title":"Knowl Data Eng IEEE Trans"},{"key":"926_CR22","doi-asserted-by":"crossref","unstructured":"Gao J, Fan W, Jiang J, Han J (2008) Knowledge transfer via multiple model local structure mapping. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 283\u2013291","DOI":"10.1145\/1401890.1401928"},{"key":"926_CR23","doi-asserted-by":"crossref","unstructured":"Geibel P, Brefeld U, Wysotzki F (2003) Learning linear classifiers sensitive to example dependent and noisy costs. In: Advances in intelligent data analysis V, Springer, pp 167\u2013178","DOI":"10.1007\/978-3-540-45231-7_16"},{"key":"926_CR24","doi-asserted-by":"crossref","unstructured":"Gong P, Ye J, Zhang C (2012) Robust multi-task feature learning. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 895\u2013903","DOI":"10.1145\/2339530.2339672"},{"issue":"1","key":"926_CR25","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/s10618-011-0244-8","volume":"26","author":"SK Gupta","year":"2013","unstructured":"Gupta SK, Phung D, Adams B, Venkatesh S (2013) Regularized nonnegative shared subspace learning. Data Min Knowl Discov 26(1):57\u201397","journal-title":"Data Min Knowl Discov"},{"key":"926_CR26","unstructured":"Gupta SK, Phung D, Venkatesh S (2012) A slice sampler for restricted hierarchical beta process with applications to shared subspace learning. In: Proceedings of the twenty-eighth conference on uncertainty in artificial intelligence, Catalina Island, CA, USA, 14\u201318 Aug 2012, pp 316\u2013325"},{"key":"926_CR27","unstructured":"Gupta SK, Phung D, Venkatesh S (2013) Factorial multi-task learning: a bayesian nonparametric approach. In: International conference on machine learning, pp 657\u2013665"},{"key":"926_CR28","doi-asserted-by":"crossref","unstructured":"Gupta SK, Rana S, Phung D, Venkatesh S (2015) Collaborating differently on different topics: A multi-relational approach to multi-task learning. In: Advances in knowledge discovery and data mining, Ho Chi Minh City, Vietnam. Springer, Berlin Heidelberg, pp 303\u2013316","DOI":"10.1007\/978-3-319-18038-0_24"},{"key":"926_CR29","doi-asserted-by":"crossref","unstructured":"Gupta SK, Rana S, Phung D, Venkatesh S (2015) What shall I share and with whom? A multi-task learning formulation using multi-faceted task relationships. In: Proceedings of the SIAM international conference on data mining, Vancouver, Canada, pp 703\u2013711","DOI":"10.1137\/1.9781611974010.79"},{"key":"926_CR30","unstructured":"Jawanpuria P, Nath JS (2012) A convex feature learning formulation for latent task structure discovery. In: Proceedings of the 29th international conference on machine learning (ICML)"},{"key":"926_CR31","doi-asserted-by":"crossref","unstructured":"Jebara T (2004) Multi-task feature and kernel selection for svms. In: Proceedings of the twenty-first international conference on machine learning, ACM, p 55","DOI":"10.1145\/1015330.1015426"},{"key":"926_CR32","unstructured":"Kang Z, Grauman K, Sha F (2011) Learning with whom to share in multi-task feature learning. In: Proceedings of the 28th international conference on machine learning, pp 521\u2013528"},{"key":"926_CR33","unstructured":"Kumar A, Daum\u00e9 III H (2012) Learning task grouping and overlap in multi-task learning. In: International conference on machine learning (ICML)"},{"key":"926_CR34","doi-asserted-by":"crossref","unstructured":"Lawrence ND, Platt JC (2004) Learning to learn with the informative vector machine. In: Proceedings of the twenty-first international conference on machine learning, ACM, p 65","DOI":"10.1145\/1015330.1015382"},{"key":"926_CR35","unstructured":"Lee H, Battle A, Raina R, Ng AY (2006) Efficient sparse coding algorithms. In: Advances in neural information processing systems, pp 801\u2013808"},{"key":"926_CR36","doi-asserted-by":"crossref","unstructured":"Lee S-I, Chatalbashev V, Vickrey D, Koller D (2007) Learning a meta-level prior for feature relevance from multiple related tasks. In: Proceedings of the 24th international conference on machine learning, ACM, pp 489\u2013496","DOI":"10.1145\/1273496.1273558"},{"key":"926_CR37","doi-asserted-by":"crossref","unstructured":"Lenarcik A, Piasta Z (1998) Rough classifiers sensitive to costs varying from object to object. In: Rough sets and current trends in computing, Springer, pp 222\u2013230","DOI":"10.1007\/3-540-69115-4_31"},{"issue":"2","key":"926_CR38","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1287\/mksc.15.2.173","volume":"15","author":"PJ Lenk","year":"1996","unstructured":"Lenk PJ, De Sarbo WS, Green PE, Young MR (1996) Hierarchical bayes conjoint analysis: recovery of partworth heterogeneity from reduced experimental designs. Mark Sci 15(2):173\u2013191","journal-title":"Mark Sci"},{"issue":"1","key":"926_CR39","doi-asserted-by":"crossref","first-page":"66","DOI":"10.3923\/ajms.2011.66.70","volume":"4","author":"S Li","year":"2011","unstructured":"Li S (2011) Concise formulas for the area and volume of a hyperspherical cap. Asian J Math Stat 4(1):66\u201370","journal-title":"Asian J Math Stat"},{"key":"926_CR40","doi-asserted-by":"crossref","unstructured":"Liao X, Xue Y, Carin L (2005) Logistic regression with an auxiliary data source. In: Proceedings of the 22nd international conference on machine learning, ACM, pp 505\u2013512","DOI":"10.1145\/1102351.1102415"},{"key":"926_CR41","doi-asserted-by":"crossref","unstructured":"Ling X, Dai W, Xue G-R, Yang Q, Yu Y (2008) Spectral domain-transfer learning. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 488\u2013496","DOI":"10.1145\/1401890.1401951"},{"key":"926_CR42","volume-title":"Directional statistics","author":"KV Mardia","year":"2009","unstructured":"Mardia KV, Jupp PE (2009) Directional statistics, vol 494. Wiley, New York"},{"key":"926_CR43","unstructured":"McCallum A, Nigam K (1998) A comparison of event models for naive bayes text classification. In: AAAI-98 workshop on learning for text categorization, vol 752, Citeseer, pp 41\u201348"},{"key":"926_CR44","unstructured":"Mihalkova L, Huynh T, Mooney RJ (2007) Mapping and revising markov logic networks for transfer learning. In: AAAI, vol\u00a07, pp 608\u2013614"},{"issue":"10","key":"926_CR45","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"926_CR46","unstructured":"Passos A, Rai P, Wainer J, Daume\u00a0III H (2012) Flexible modeling of latent task structures in multitask learning. arXiv preprint arXiv:1206.6486"},{"key":"926_CR47","doi-asserted-by":"crossref","unstructured":"Pavlov D, Balasubramanyan R, Dom B, Kapur S, Parikh J (2004) Document preprocessing for naive bayes classification and clustering with mixture of multinomials. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 829\u2013834","DOI":"10.1145\/1014052.1016922"},{"key":"926_CR48","doi-asserted-by":"crossref","unstructured":"Pearl J (2012) Some thoughts concerning transfer learning, with applications to meta-analysis and data-sharing estimation. Technical report, Technical Report Technical Report r-387, cognitive systems laboratory, Department of Computer Science, UCLA","DOI":"10.2139\/ssrn.2343866"},{"key":"926_CR49","unstructured":"Platt JC (1999) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in large margin classifiers. Citeseer"},{"key":"926_CR50","doi-asserted-by":"crossref","unstructured":"Raina R, Battle A, Lee H, Packer B, Ng AY (2007) Self-taught learning: transfer learning from unlabeled data. In: proceedings of the 24th international conference on machine learning, ACM, pp 759\u2013766","DOI":"10.1145\/1273496.1273592"},{"key":"926_CR51","unstructured":"Saha B, Gupta SK, Phung D, Venkatesh S (2014) Multiple task transfer learning with small sample sizes. In: Knowledge and information systems, pp 1\u201328"},{"issue":"2","key":"926_CR52","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/S0378-3758(00)00115-4","volume":"90","author":"H Shimodaira","year":"2000","unstructured":"Shimodaira H (2000) Improving predictive inference under covariate shift by weighting the log-likelihood function. J Stat Plan Inference 90(2):227\u2013244","journal-title":"J Stat Plan Inference"},{"key":"926_CR53","unstructured":"Thrun S (1996) Learning to learn: introduction. In: Learning to learn, Citeseer"},{"issue":"3","key":"926_CR54","doi-asserted-by":"crossref","first-page":"e34312","DOI":"10.1371\/journal.pone.0034312","volume":"7","author":"VMCA Belle Van","year":"2012","unstructured":"Van Belle VMCA, Van Calster B, Timmerman D, Bourne T, Bottomley C, Valentin L, Neven P, Van Huffel S, Suykens JAK, Boyd S (2012) A mathematical model for interpretable clinical decision support with applications in gynecology. PloS one 7(3):e34312","journal-title":"PloS one"},{"key":"926_CR55","doi-asserted-by":"crossref","unstructured":"Wang Q, Zhang L, Chi M, Guo J (2008) MTForest: ensemble decision trees based on multi-task learning. In: European conference on artificial intelligence (ECAI), pp 122\u2013126","DOI":"10.3233\/978-1-58603-891-5-122"},{"key":"926_CR56","doi-asserted-by":"crossref","unstructured":"Wang Z, Song Y, Zhang C (2008) Transferred dimensionality reduction. In: machine learning and knowledge discovery in databases, Springer, pp 550\u2013565","DOI":"10.1007\/978-3-540-87481-2_36"},{"key":"926_CR57","doi-asserted-by":"crossref","unstructured":"Wu P, Dietterich TG (2004) Improving svm accuracy by training on auxiliary data sources. In: Proceedings of the twenty-first international conference on machine learning, ACM, p 110","DOI":"10.1145\/1015330.1015436"},{"key":"926_CR58","first-page":"35","volume":"8","author":"Y Xue","year":"2007","unstructured":"Xue Y, Liao X, Carin L, Krishnapuram B (2007) Multi-task learning for classification with dirichlet process priors. J Mach Learn Res 8:35\u201363","journal-title":"J Mach Learn Res"},{"key":"926_CR59","doi-asserted-by":"crossref","unstructured":"Yang J, Yan R, Hauptmann AG (2007) Cross-domain video concept detection using adaptive svms. In: Proceedings of the 15th international conference on multimedia, pp 188\u2013197","DOI":"10.1145\/1291233.1291276"},{"key":"926_CR60","doi-asserted-by":"crossref","unstructured":"Yu K, Tresp V, Schwaighofer A (2005) Learning gaussian processes from multiple tasks. In: Proceedings of the 22nd international conference on Machine learning, ACM, pp 1012\u20131019","DOI":"10.1145\/1102351.1102479"},{"key":"926_CR61","doi-asserted-by":"crossref","unstructured":"Zadrozny B (2004) Learning and evaluating classifiers under sample selection bias. In: Proceedings of the twenty-first international conference on Machine learning, ACM, p 114","DOI":"10.1145\/1015330.1015425"},{"key":"926_CR62","doi-asserted-by":"crossref","unstructured":"Zadrozny B, Langford J, Abe N (2003) Cost-sensitive learning by cost-proportionate example weighting. In: Third IEEE international conference on data mining, 2003 (ICDM 2003), IEEE, pp 435\u2013442","DOI":"10.1109\/ICDM.2003.1250950"},{"key":"926_CR63","unstructured":"Zhang Y, Yeung D-Y (2010) A convex formulation for learning task relationships in multi-task learning. In: UAI, pp 733\u2013442"},{"key":"926_CR64","doi-asserted-by":"crossref","unstructured":"Zhou J, Sun J, Liu Y, Hu J, Ye J (2013) Patient risk prediction model via top-k stability selection. In: SIAM conference on data mining. SIAM","DOI":"10.1137\/1.9781611972832.7"},{"key":"926_CR65","unstructured":"Zhu J, Chen N, Xing EP (2011) Infinite latent svm for classification and multi-task learning. In: NIPS, pp 1620\u20131628"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-016-0926-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10115-016-0926-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-016-0926-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-016-0926-z","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T12:49:54Z","timestamp":1748782194000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10115-016-0926-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,2,19]]},"references-count":65,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2016,12]]}},"alternative-id":["926"],"URL":"https:\/\/doi.org\/10.1007\/s10115-016-0926-z","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"type":"print","value":"0219-1377"},{"type":"electronic","value":"0219-3116"}],"subject":[],"published":{"date-parts":[[2016,2,19]]}}}