{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:23:01Z","timestamp":1750220581916,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":37,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T00:00:00Z","timestamp":1597881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100006642","name":"U.S. Department of Education","doi-asserted-by":"publisher","award":["P200A150306"],"award-info":[{"award-number":["P200A150306"]}],"id":[{"id":"10.13039\/100006642","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-1718310, IIS-1815866, CNS-1852498, CNS-1560229"],"award-info":[{"award-number":["IIS-1718310, IIS-1815866, CNS-1852498, CNS-1560229"]}],"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":[[2020,8,23]]},"DOI":"10.1145\/3394486.3403191","type":"proceedings-article","created":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T23:18:56Z","timestamp":1597965536000},"page":"1382-1392","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Recurrent Halting Chain for Early Multi-label Classification"],"prefix":"10.1145","author":[{"given":"Thomas","family":"Hartvigsen","sequence":"first","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cansu","family":"Sen","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangnan","family":"Kong","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elke","family":"Rundensteiner","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,8,20]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"D. Anguita A. Ghio L. Oneto X. Parra and J. Reyes-Ortiz. 2013. A public domain dataset for human activity recognition using smartphones.. In ESANN.  D. Anguita A. Ghio L. Oneto X. Parra and J. Reyes-Ortiz. 2013. A public domain dataset for human activity recognition using smartphones.. In ESANN."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2004.03.009"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"crossref","unstructured":"Y.-C. Chen S.-F.and Chen C.-K. Yeh and Y.-C. Wang. 2018. Order-free RNN with visual attention for multi-label classification. In AAAI.  Y.-C. Chen S.-F.and Chen C.-K. Yeh and Y.-C. Wang. 2018. Order-free RNN with visual attention for multi-label classification. In AAAI.","DOI":"10.1609\/aaai.v32i1.12230"},{"key":"e_1_3_2_2_4_1","unstructured":"D. Dennis C. Pabbaraju H. Simhadri and P. Jain. 2018. Multiple instance learning for efficient sequential data classification on resource-constrained devices. In NeurIPS. 10953--10964.  D. Dennis C. Pabbaraju H. Simhadri and P. Jain. 2018. Multiple instance learning for efficient sequential data classification on resource-constrained devices. In NeurIPS. 10953--10964."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"crossref","unstructured":"Y. Fujita N. Kanda S. Horiguchi K. Nagamatsu and S. Watanabe. 2019. End-to-End Neural Speaker Diarization with Permutation-Free Objectives. In Interspeech.  Y. Fujita N. Kanda S. Horiguchi K. Nagamatsu and S. Watanabe. 2019. End-to-End Neural Speaker Diarization with Permutation-Free Objectives. In Interspeech.","DOI":"10.21437\/Interspeech.2019-2899"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-13-195"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"crossref","unstructured":"M. Ghalwash V. Radosavljevic and Z. Obradovic. 2013. Extraction of interpretable multivariate patterns for early diagnostics. In ICDM. 201--210.  M. Ghalwash V. Radosavljevic and Z. Obradovic. 2013. Extraction of interpretable multivariate patterns for early diagnostics. In ICDM. 201--210.","DOI":"10.1109\/ICDM.2013.19"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"crossref","unstructured":"M. Ghalwash V. Radosavljevic and Z. Obradovic. 2014. Utilizing temporal patterns for estimating uncertainty in interpretable early decision making. In SIGKDD. 402--411.  M. Ghalwash V. Radosavljevic and Z. Obradovic. 2014. Utilizing temporal patterns for estimating uncertainty in interpretable early decision making. In SIGKDD. 402--411.","DOI":"10.1145\/2623330.2623694"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"crossref","unstructured":"A. Gupta H. P. Gupta B. Biswas and T. Dutta. 2020. An Early Classification Approach for Multivariate Time Series of On-Vehicle Sensors in Transportation. IEEE Transactions on Intelligent Transportation Systems (2020).  A. Gupta H. P. Gupta B. Biswas and T. Dutta. 2020. An Early Classification Approach for Multivariate Time Series of On-Vehicle Sensors in Transportation. IEEE Transactions on Intelligent Transportation Systems (2020).","DOI":"10.1109\/TITS.2019.2957325"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"crossref","unstructured":"T. Hartvigsen C. Sen S. Brownell E. Teeple X. Kong and E. Rundensteiner. 2018. Early Prediction of MRSA Infections using Electronic Health Records. In HEALTHINF. 156--167.  T. Hartvigsen C. Sen S. Brownell E. Teeple X. Kong and E. Rundensteiner. 2018. Early Prediction of MRSA Infections using Electronic Health Records. In HEALTHINF. 156--167.","DOI":"10.5220\/0006599601560167"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"crossref","unstructured":"T. Hartvigsen C. Sen X. Kong and E. Rundensteiner. 2019. Adaptive-Halting Policy Network for Early Classification. In SIGKDD. 101--110.  T. Hartvigsen C. Sen X. Kong and E. Rundensteiner. 2019. Adaptive-Halting Policy Network for Early Classification. In SIGKDD. 101--110.","DOI":"10.1145\/3292500.3330974"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2014.07.056"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"crossref","unstructured":"Z. Huang Z. Ye S. Li and R. Pan. 2017. Length Adaptive Recurrent Model for Text Classification. In CIKM. 1019--1027.  Z. Huang Z. Ye S. Li and R. Pan. 2017. Length Adaptive Recurrent Model for Text Classification. In CIKM. 1019--1027.","DOI":"10.1145\/3132847.3132947"},{"key":"e_1_3_2_2_15_1","volume-title":"Adam: A method for stochastic optimization. In ICLR.","author":"Kingma D.","year":"2014","unstructured":"D. Kingma and J. Ba . 2014 . Adam: A method for stochastic optimization. In ICLR. D. Kingma and J. Ba. 2014. Adam: A method for stochastic optimization. In ICLR."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"crossref","unstructured":"Y.-F. Lin H.-H. Chen V. Tseng and J. Pei. 2015. Reliable early classification on multivariate time series with numerical and categorical attributes. In PAKDD. 199--211.  Y.-F. Lin H.-H. Chen V. Tseng and J. Pei. 2015. Reliable early classification on multivariate time series with numerical and categorical attributes. In PAKDD. 199--211.","DOI":"10.1007\/978-3-319-18038-0_16"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"crossref","unstructured":"S. Ma L. Sigal and S. Sclaroff. 2016. Learning activity progression in lstms for activity detection and early detection. In CVPR. 1942--1950.  S. Ma L. Sigal and S. Sclaroff. 2016. Learning activity progression in lstms for activity detection and early detection. In CVPR. 1942--1950.","DOI":"10.1109\/CVPR.2016.214"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"crossref","unstructured":"C. Martinez E. Ramasso G. Perrin and M. Rombaut. 2019. Adaptive early classification of temporal sequences using deep reinforcement learning. Knowledge-Based Systems (2019).  C. Martinez E. Ramasso G. Perrin and M. Rombaut. 2019. Adaptive early classification of temporal sequences using deep reinforcement learning. Knowledge-Based Systems (2019).","DOI":"10.1016\/j.knosys.2019.105290"},{"key":"e_1_3_2_2_19_1","unstructured":"V. Mnih K. Kavukcuoglu D. Silver A. Graves I. Antonoglou D. Wierstra and M. Riedmiller. 2013. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 (2013).  V. Mnih K. Kavukcuoglu D. Silver A. Graves I. Antonoglou D. Wierstra and M. Riedmiller. 2013. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 (2013)."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"crossref","unstructured":"U. Mori A. Mendiburu S. Dasgupta and J. Lozano. 2018. Early classification of time series by simultaneously optimizing the accuracy and earliness. IEEE transactions on neural networks and learning systems Vol. 29 10 (2018) 4569 -- 4578.  U. Mori A. Mendiburu S. Dasgupta and J. Lozano. 2018. Early classification of time series by simultaneously optimizing the accuracy and earliness. IEEE transactions on neural networks and learning systems Vol. 29 10 (2018) 4569 -- 4578.","DOI":"10.1109\/TNNLS.2017.2764939"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-016-0462-1"},{"key":"e_1_3_2_2_22_1","unstructured":"J. Nam Y.-B. Kim E. Mencia S. Park R. Sarikaya and J. F\u00fcrnkranz. 2019. Learning Context-dependent Label Permutations for Multi-label Classification. In ICML. 4733--4742.  J. Nam Y.-B. Kim E. Mencia S. Park R. Sarikaya and J. F\u00fcrnkranz. 2019. Learning Context-dependent Label Permutations for Multi-label Classification. In ICML. 4733--4742."},{"key":"e_1_3_2_2_23_1","unstructured":"J. Nam E. Menc'ia H. Kim and J. F\u00fcrnkranz. 2017. Maximizing subset accuracy with recurrent neural networks in multi-label classification. In NeurIPS. 5413--5423.  J. Nam E. Menc'ia H. Kim and J. F\u00fcrnkranz. 2017. Maximizing subset accuracy with recurrent neural networks in multi-label classification. In NeurIPS. 5413--5423."},{"key":"e_1_3_2_2_24_1","unstructured":"J. Schulman N. Heess T. Weber and P. Abbeel. 2015. Gradient estimation using stochastic computation graphs. In NeurIPS. 3528--3536.  J. Schulman N. Heess T. Weber and P. Abbeel. 2015. Gradient estimation using stochastic computation graphs. In NeurIPS. 3528--3536."},{"key":"e_1_3_2_2_25_1","volume-title":"J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al.","author":"Silver D.","year":"2016","unstructured":"D. Silver , A. Huang , C. Maddison , A. Guez , L. Sifre , G. Van Den Driessche , J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al. 2016 . Mastering the game of Go with deep neural networks and tree search. nature, Vol. 529 , 7587 (2016), 484. D. Silver, A. Huang, C. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al. 2016. Mastering the game of Go with deep neural networks and tree search. nature, Vol. 529, 7587 (2016), 484."},{"key":"e_1_3_2_2_26_1","unstructured":"R. Sutton D. McAllester S. Singh and Y. Mansour. 2000. Policy gradient methods for reinforcement learning with function approximation. In NeurIPS. 1057--1063.  R. Sutton D. McAllester S. Singh and Y. Mansour. 2000. Policy gradient methods for reinforcement learning with function approximation. In NeurIPS. 1057--1063."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"crossref","unstructured":"C.-P. Tsai and H.-Y. Lee. 2020. Order-free Learning Alleviating Exposure Bias in Multi-label Classification. In AAAI.  C.-P. Tsai and H.-Y. Lee. 2020. Order-free Learning Alleviating Exposure Bias in Multi-label Classification. In AAAI.","DOI":"10.1609\/aaai.v34i04.6066"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/MPRV.2017.3971131"},{"key":"e_1_3_2_2_29_1","unstructured":"O. Vinyals S. Bengio and M. Kudlur. 2017. Order matters: Sequence to sequence for sets. In ICLR.  O. Vinyals S. Bengio and M. Kudlur. 2017. Order matters: Sequence to sequence for sets. In ICLR."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"crossref","unstructured":"J. Wang Y. Yang J. Mao Z. Huang C. Huang and W. Xu. 2016. CNN-RNN: A unified framework for multi-label image classification. In CVPR. 2285--2294.  J. Wang Y. Yang J. Mao Z. Huang C. Huang and W. Xu. 2016. CNN-RNN: A unified framework for multi-label image classification. In CVPR. 2285--2294.","DOI":"10.1109\/CVPR.2016.251"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992696"},{"key":"e_1_3_2_2_32_1","volume-title":"Time Series: A Nearest Neighbor Approach. In IJCAI. 1297--1302.","author":"Xing Z.","year":"2009","unstructured":"Z. Xing , J. Pei , and P. Yu . 2009 . Early Prediction on Time Series: A Nearest Neighbor Approach. In IJCAI. 1297--1302. Z. Xing, J. Pei, and P. Yu. 2009. Early Prediction on Time Series: A Nearest Neighbor Approach. In IJCAI. 1297--1302."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.5555\/3225640.3225814"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"crossref","unstructured":"Z. Xing J. Pei P. Yu and K. Wang. 2011. Extracting interpretable features for early classification on time series. In SDM. 247--258.  Z. Xing J. Pei P. Yu and K. Wang. 2011. Extracting interpretable features for early classification on time series. In SDM. 247--258.","DOI":"10.1137\/1.9781611972818.22"},{"key":"e_1_3_2_2_35_1","volume-title":"SGM: Sequence Generation Model for Multi-label Classification. In COLING. 3915--3926.","author":"Yang P.","year":"2018","unstructured":"P. Yang , X. Sun , W. Li , S. Ma , W. Wu , and H. Wang . 2018 . SGM: Sequence Generation Model for Multi-label Classification. In COLING. 3915--3926. P. Yang, X. Sun, W. Li, S. Ma, W. Wu, and H. Wang. 2018. SGM: Sequence Generation Model for Multi-label Classification. In COLING. 3915--3926."},{"key":"e_1_3_2_2_36_1","unstructured":"L. Yao E. Poblenz D. Dagunts B. Covington D. Bernard and K. Lyman. 2017. Learning to diagnose from scratch by exploiting dependencies among labels. arXiv preprint arXiv:1710.10501 (2017).  L. Yao E. Poblenz D. Dagunts B. Covington D. Bernard and K. Lyman. 2017. Learning to diagnose from scratch by exploiting dependencies among labels. arXiv preprint arXiv:1710.10501 (2017)."},{"key":"e_1_3_2_2_37_1","unstructured":"W.\n      Zhang D.\n      Jha E.\n      Laftchiev and \n      D.\n      Nikovski\n  . \n  2020\n  . Multi-label Prediction in \n  Time Series Data\n   using \n  Deep Neural Networks\n  . arXiv preprint Vol. abs\/\n  2001\n  .10098 (2020).  W. Zhang D. Jha E. Laftchiev and D. Nikovski. 2020. Multi-label Prediction in Time Series Data using Deep Neural Networks. arXiv preprint Vol. abs\/2001.10098 (2020)."}],"event":{"name":"KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"location":"Virtual Event CA USA","acronym":"KDD '20"},"container-title":["Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403191","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3394486.3403191","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3394486.3403191","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:31:34Z","timestamp":1750195894000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403191"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,20]]},"references-count":37,"alternative-id":["10.1145\/3394486.3403191","10.1145\/3394486"],"URL":"https:\/\/doi.org\/10.1145\/3394486.3403191","relation":{},"subject":[],"published":{"date-parts":[[2020,8,20]]},"assertion":[{"value":"2020-08-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}