{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T19:16:48Z","timestamp":1726514208746},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030375980"},{"type":"electronic","value":"9783030375997"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-37599-7_50","type":"book-chapter","created":{"date-parts":[[2020,1,3]],"date-time":"2020-01-03T14:02:43Z","timestamp":1578060163000},"page":"605-618","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-task Learning by Pareto Optimality"],"prefix":"10.1007","author":[{"given":"Deyan","family":"Dyankov","sequence":"first","affiliation":[]},{"given":"Salvatore Danilo","family":"Riccio","sequence":"additional","affiliation":[]},{"given":"Giuseppe","family":"Di Fatta","sequence":"additional","affiliation":[]},{"given":"Giuseppe","family":"Nicosia","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,1,3]]},"reference":[{"key":"50_CR1","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.tcs.2011.03.012","volume":"425","author":"A Auger","year":"2012","unstructured":"Auger, A., Bader, J., Brockhoff, D., Zitzler, E.: Hypervolume-based multiobjective optimization: theoretical foundations and practical implications. Theoret. Comput. Sci. 425, 75\u2013103 (2012)","journal-title":"Theoret. Comput. Sci."},{"key":"50_CR2","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1613\/jair.3912","volume":"47","author":"MG Bellemare","year":"2013","unstructured":"Bellemare, M.G., Naddaf, Y., Veness, J., Bowling, M.: The arcade learning environment: an evaluation platform for general agents. J. Artif. Intell. Res. 47, 253\u2013259 (2013)","journal-title":"J. Artif. Intell. Res."},{"key":"50_CR3","unstructured":"Brockman, G., et al.: Openai gym (2016)"},{"key":"50_CR4","doi-asserted-by":"publisher","unstructured":"Caruana R.: Multitask learning. In: Thrun S., Pratt L. (eds) Learning to Learn, pp. 95\u2013133. Springer, Boston (1998). \nhttps:\/\/doi.org\/10.1007\/978-1-4615-5529-2_5","DOI":"10.1007\/978-1-4615-5529-2_5"},{"key":"50_CR5","unstructured":"Conti, E., Madhavan, V., Petroski Such, F., Lehman, J., Stanley, K.O., Clune, J.: Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents. In: NeurIPS 2018, Montreal, Canada (2018)"},{"key":"50_CR6","unstructured":"Fonseca, C.M., Paquete, L., L\u00f3pez-Ib\u00e1\u00f1ez, M.: An improved dimension-sweep algorithm for the hypervolume indicator. In: 2006 IEEE International Conference on Evolutionary Computation, pp. 1157\u20131163 (2006)"},{"key":"50_CR7","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016) \nhttp:\/\/www.deeplearningbook.org"},{"issue":"1","key":"50_CR8","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1214\/aoms\/1177729694","volume":"22","author":"S Kullback","year":"1951","unstructured":"Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Statist. 22(1), 79\u201386 (1951). \nhttps:\/\/doi.org\/10.1214\/aoms\/1177729694","journal-title":"Ann. Math. Statist."},{"key":"50_CR9","unstructured":"Kumar, M.P., Packer, B., Koller, D.: Self-paced learning for latent variable models. In: Lafferty, J.D., Williams, C.K.I., Shawe-Taylor, J., Zemel, R.S., Culotta, A. (eds.) Advances in Neural Information Processing Systems, vol. 23, pp. 1189\u20131197. Curran Associates, Inc. (2010)"},{"key":"50_CR10","unstructured":"Maurer, A., Pontil, M., Romera-Paredes, B.: Sparse coding for multitask and transfer learning. In: Dasgupta, S., McAllester, D. (eds.) Proceedings of the 30th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 28, pp. 343\u2013351. PMLR, Atlanta, Georgia, USA, 17\u201319 June 2013. \nhttp:\/\/proceedings.mlr.press\/v28\/maurer13.html"},{"key":"50_CR11","unstructured":"Mnih, V., et al.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning (2016)"},{"issue":"7540","key":"50_CR12","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529\u2013533 (2015). \nhttps:\/\/doi.org\/10.1038\/nature14236","journal-title":"Nature"},{"key":"50_CR13","doi-asserted-by":"crossref","unstructured":"Murugesan, K., Carbonell, J.: Self-paced multitask learning with shared knowledge. IJCAI-17 (2017)","DOI":"10.24963\/ijcai.2017\/351"},{"key":"50_CR14","unstructured":"Romera-Paredes, B., Aung, H., Bianchi-Berthouze, N., Pontil, M.: Multilinear multitask learning. In: Dasgupta, S., McAllester, D. (eds.) Proceedings of the 30th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 28, pp. 1444\u20131452. PMLR, Atlanta, Georgia, USA, 17\u201319 June 2013. \nhttp:\/\/proceedings.mlr.press\/v28\/romera-paredes13.html"},{"key":"50_CR15","unstructured":"Ruder, S.: An overview of multi-task learning in deep neural networks. CoRR (2017)"},{"key":"50_CR16","unstructured":"Salimans, T., Ho, J., Chen, X., Sidor, S., Sutskever, I.: Evolution strategies as a scalable alternative to reinforcement learning. arXiv e-prints \narXiv:1703.03864\n\n, March 2017"},{"issue":"2","key":"50_CR17","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/s12559-009-9014-y","volume":"1","author":"J Schmidhuber","year":"2009","unstructured":"Schmidhuber, J.: Ultimate cognition \u00e0 la g\u00f6del. Cognitive Comput. 1(2), 177\u2013193 (2009). \nhttps:\/\/doi.org\/10.1007\/s12559-009-9014-y","journal-title":"Cognitive Comput."},{"key":"50_CR18","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1126\/science.aar6404","volume":"362","author":"D Silver","year":"2018","unstructured":"Silver, D., et al.: A general reinforcement learning algorithm that masters chess, shogi, and go through self-play. Science 362, 1140\u20131144 (2018)","journal-title":"Science"},{"key":"50_CR19","doi-asserted-by":"publisher","unstructured":"Stanley, K., Clune, J., Lehman, J., Miikkulainen, R.: Designing neural networks through neuroevolution. Nat. Mach. Intell. (2019). \nhttps:\/\/doi.org\/10.1038\/s42256-018-0006-z","DOI":"10.1038\/s42256-018-0006-z"},{"key":"50_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2010.04.005","author":"G Stracquadanio","year":"2010","unstructured":"Stracquadanio, G., Nicosia, G.: Computational energy-based redesign of robust proteins. Comput. Chem. Eng. (2010). \nhttps:\/\/doi.org\/10.1016\/j.compchemeng.2010.04.005","journal-title":"Comput. Chem. Eng."},{"issue":"1","key":"50_CR21","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1093\/nsr\/nwx105","volume":"5","author":"Y Zhang","year":"2018","unstructured":"Zhang, Y., Yang, Q.: An overview of multi-task learning. Nat. Sci. Rev. 5(1), 30\u201343 (2018). \nhttps:\/\/doi.org\/10.1093\/nsr\/nwx105","journal-title":"Nat. Sci. Rev."}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-37599-7_50","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,1,3]],"date-time":"2020-01-03T15:02:53Z","timestamp":1578063773000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-37599-7_50"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030375980","9783030375997"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-37599-7_50","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"3 January 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Optimization, and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Siena","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mod2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2019.icas.xyz\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"158","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"64","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}
