{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T16:04:20Z","timestamp":1781280260591,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":42,"publisher":"ACM","license":[{"start":{"date-parts":[[2018,2,2]],"date-time":"2018-02-02T00:00:00Z","timestamp":1517529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-1320617 and IIS 16-18481"],"award-info":[{"award-number":["IIS-1320617 and IIS 16-18481"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"trans-NIH Big Data to Knowledge (BD2K) initiative","award":["1U54GM114838"],"award-info":[{"award-number":["1U54GM114838"]}]},{"name":"U.S. Army Research Lab.","award":["W911NF-09-2-0053 (NSCTA)"],"award-info":[{"award-number":["W911NF-09-2-0053 (NSCTA)"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2018,2,2]]},"DOI":"10.1145\/3159652.3159711","type":"proceedings-article","created":{"date-parts":[[2018,2,6]],"date-time":"2018-02-06T18:12:23Z","timestamp":1517940743000},"page":"468-476","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":33,"title":["Curriculum Learning for Heterogeneous Star Network Embedding via Deep Reinforcement Learning"],"prefix":"10.1145","author":[{"given":"Meng","family":"Qu","sequence":"first","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Urbana, IL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Tang","sequence":"additional","affiliation":[{"name":"HEC Montreal&Montreal Institute for Learning Algorithms, Montreal, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawei","family":"Han","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Urbana, IL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,2,2]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1013689704352"},{"key":"e_1_3_2_1_2_1","volume-title":"DTIC Document","author":"Bellman R.","year":"1957","unstructured":"R. Bellman . A markovian decision process. Technical report , DTIC Document , 1957 . R. Bellman. A markovian decision process. Technical report, DTIC Document, 1957."},{"key":"e_1_3_2_1_3_1","first-page":"1171","volume-title":"Advances in Neural Information Processing Systems","author":"Bengio S.","year":"2015","unstructured":"S. Bengio , O. Vinyals , N. Jaitly , and N. Shazeer . Scheduled sampling for sequence prediction with recurrent neural networks . In Advances in Neural Information Processing Systems , pages 1171 -- 1179 , 2015 . S. Bengio, O. Vinyals, N. Jaitly, and N. Shazeer. Scheduled sampling for sequence prediction with recurrent neural networks. In Advances in Neural Information Processing Systems, pages 1171--1179, 2015."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553380"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3018661.3018735"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/0010-0277(93)90058-4"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/1390681.1442794"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273531"},{"key":"e_1_3_2_1_9_1","volume-title":"Modification of uct with patterns in monte-carlo go","author":"Gelly S.","year":"2006","unstructured":"S. Gelly , Y. Wang , O. Teytaud , M. U. Patterns , and P. Tao . Modification of uct with patterns in monte-carlo go . 2006 . S. Gelly, Y. Wang, O. Teytaud, M. U. Patterns, and P. Tao. Modification of uct with patterns in monte-carlo go. 2006."},{"key":"e_1_3_2_1_10_1","volume-title":"Scheduled denoising autoencoders. arXiv preprint arXiv:1406.3269","author":"Geras K. J.","year":"2014","unstructured":"K. J. Geras and C. Sutton . Scheduled denoising autoencoders. arXiv preprint arXiv:1406.3269 , 2014 . K. J. Geras and C. Sutton. Scheduled denoising autoencoders. arXiv preprint arXiv:1406.3269, 2014."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0111"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/11871842_29"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995523"},{"key":"e_1_3_2_1_16_1","volume-title":"Attributed network embedding for learning in a dynamic environment. arXiv preprint arXiv:1706.01860","author":"Li J.","year":"2017","unstructured":"J. Li , H. Dani , X. Hu , J. Tang , Y. Chang , and H. Liu . Attributed network embedding for learning in a dynamic environment. arXiv preprint arXiv:1706.01860 , 2017 . J. Li, H. Dani, X. Hu, J. Tang, Y. Chang, and H. Liu. Attributed network embedding for learning in a dynamic environment. arXiv preprint arXiv:1706.01860, 2017."},{"key":"e_1_3_2_1_17_1","volume-title":"Deep reinforcement learning for dialogue generation. arXiv preprint arXiv:1606.01541","author":"Li J.","year":"2016","unstructured":"J. Li , W. Monroe , A. Ritter , M. Galley , J. Gao , and D. Jurafsky . Deep reinforcement learning for dialogue generation. arXiv preprint arXiv:1606.01541 , 2016 . J. Li, W. Monroe, A. Ritter, M. Galley, J. Gao, and D. Jurafsky. Deep reinforcement learning for dialogue generation. arXiv preprint arXiv:1606.01541, 2016."},{"key":"e_1_3_2_1_18_1","volume-title":"Learning multi-faceted representations of individuals from heterogeneous evidence using neural networks. arXiv preprint arXiv:1510.05198","author":"Li J.","year":"2015","unstructured":"J. Li , A. Ritter , and D. Jurafsky . Learning multi-faceted representations of individuals from heterogeneous evidence using neural networks. arXiv preprint arXiv:1510.05198 , 2015 . J. Li, A. Ritter, and D. Jurafsky. Learning multi-faceted representations of individuals from heterogeneous evidence using neural networks. arXiv preprint arXiv:1510.05198, 2015."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/DAS.2014.38"},{"key":"e_1_3_2_1_20_1","first-page":"3111","volume-title":"Advances in neural information processing systems","author":"Mikolov T.","year":"2013","unstructured":"T. Mikolov , I. Sutskever , K. Chen , G. S. Corrado , and J. Dean . Distributed representations of words and phrases and their compositionality . In Advances in neural information processing systems , pages 3111 -- 3119 , 2013 . T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, pages 3111--3119, 2013."},{"key":"e_1_3_2_1_21_1","volume-title":"A fast and simple algorithm for training neural probabilistic language models. arXiv preprint arXiv:1206.6426","author":"Mnih A.","year":"2012","unstructured":"A. Mnih and Y. W. Teh . A fast and simple algorithm for training neural probabilistic language models. arXiv preprint arXiv:1206.6426 , 2012 . A. Mnih and Y. W. Teh. A fast and simple algorithm for training neural probabilistic language models. arXiv preprint arXiv:1206.6426, 2012."},{"key":"e_1_3_2_1_22_1","first-page":"2204","volume-title":"Advances in neural information processing systems","author":"Mnih V.","year":"2014","unstructured":"V. Mnih , N. Heess , A. Graves , Recurrent models of visual attention . In Advances in neural information processing systems , pages 2204 -- 2212 , 2014 . V. Mnih, N. Heess, A. Graves, et al. Recurrent models of visual attention. In Advances in neural information processing systems, pages 2204--2212, 2014."},{"key":"e_1_3_2_1_23_1","volume-title":"Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602","author":"Mnih V.","year":"2013","unstructured":"V. Mnih , K. Kavukcuoglu , D. Silver , A. Graves , I. Antonoglou , D. Wierstra , and M. Riedmiller . 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. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1561\/9781601987679"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299188"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCIAIG.2013.2291577"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3133021"},{"key":"e_1_3_2_1_29_1","unstructured":"D. Silver A. Huang C. J. Maddison A. Guez L. Sifre G. Van Den Driessche J. Schrittwieser I. Antonoglou V. Panneershelvam M. Lanctot et al. Mastering the game of go with deep neural networks and tree search. Nature.  D. Silver A. Huang C. J. Maddison A. Guez L. Sifre G. Van Den Driessche J. Schrittwieser I. Antonoglou V. Panneershelvam M. Lanctot et al. Mastering the game of go with deep neural networks and tree search. Nature."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390278"},{"key":"e_1_3_2_1_31_1","first-page":"1","volume-title":"Representation of Language and Language Learning","author":"Spitkovsky V. I.","year":"2009","unstructured":"V. I. Spitkovsky , H. Alshawi , and D. Jurafsky . Baby steps: How ?less is more? in unsupervised dependency parsing. NIPS: Grammar Induction , Representation of Language and Language Learning , pages 1 -- 10 , 2009 . V. I. Spitkovsky, H. Alshawi, and D. Jurafsky. Baby steps: How ?less is more? in unsupervised dependency parsing. NIPS: Grammar Induction, Representation of Language and Language Learning, pages 1--10, 2009."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.14778\/3402707.3402736"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557107"},{"key":"e_1_3_2_1_34_1","unstructured":"R. S. Sutton. Integrated architectures for learning planning and reacting based on approximating dynamic programming.  R. S. Sutton. Integrated architectures for learning planning and reacting based on approximating dynamic programming."},{"key":"e_1_3_2_1_35_1","volume-title":"Reinforcement learning: An introduction","author":"Sutton R. S.","unstructured":"R. S. Sutton and A. G. Barto . Reinforcement learning: An introduction , volume 1 . R. S. Sutton and A. G. Barto. Reinforcement learning: An introduction, volume 1."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/2872427.2883041"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783307"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1402008"},{"key":"e_1_3_2_1_40_1","volume-title":"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural networks for machine learning, 4(2):26--31","author":"Tieleman T.","year":"2012","unstructured":"T. Tieleman and G. Hinton . Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural networks for machine learning, 4(2):26--31 , 2012 . T. Tieleman and G. Hinton. Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural networks for machine learning, 4(2):26--31, 2012."},{"key":"e_1_3_2_1_41_1","volume-title":"Learning the curriculum with bayesian optimization for task-specific word representation learning. arXiv preprint arXiv:1605.03852","author":"Tsvetkov Y.","year":"2016","unstructured":"Y. Tsvetkov , M. Faruqui , W. Ling , and C. Dyer . Learning the curriculum with bayesian optimization for task-specific word representation learning. arXiv preprint arXiv:1605.03852 , 2016 . Y. Tsvetkov, M. Faruqui, W. Ling, and C. Dyer. Learning the curriculum with bayesian optimization for task-specific word representation learning. arXiv preprint arXiv:1605.03852, 2016."},{"key":"e_1_3_2_1_42_1","volume-title":"Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578","author":"Zoph B.","year":"2016","unstructured":"B. Zoph and Q. V. Le . Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578 , 2016 . B. Zoph and Q. V. Le. Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578, 2016."}],"event":{"name":"WSDM 2018: The Eleventh ACM International Conference on Web Search and Data Mining","location":"Marina Del Rey CA USA","acronym":"WSDM 2018","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3159652.3159711","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3159652.3159711","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3159652.3159711","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T02:26:12Z","timestamp":1750213572000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3159652.3159711"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,2,2]]},"references-count":42,"alternative-id":["10.1145\/3159652.3159711","10.1145\/3159652"],"URL":"https:\/\/doi.org\/10.1145\/3159652.3159711","relation":{},"subject":[],"published":{"date-parts":[[2018,2,2]]},"assertion":[{"value":"2018-02-02","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}