{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T11:16:08Z","timestamp":1730200568083,"version":"3.28.0"},"reference-count":29,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1109\/bigdata47090.2019.9006010","type":"proceedings-article","created":{"date-parts":[[2020,2,25]],"date-time":"2020-02-25T01:05:34Z","timestamp":1582592734000},"page":"5611-5619","source":"Crossref","is-referenced-by-count":2,"title":["Advertiser-Assisted Behavioral Ad-Targeting via Denoised Distribution Induction"],"prefix":"10.1109","author":[{"given":"Kei","family":"Yonekawa","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Niu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mori","family":"Kurokawa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arei","family":"Kobayashi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daichi","family":"Amagata","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takuya","family":"Maekawa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takahiro","family":"Hara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","first-page":"1","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"ganin","year":"2016","journal-title":"Journal of Machine Learning Research"},{"key":"ref11","article-title":"Adam: a method for stochastic optimization","author":"kingma","year":"2015","journal-title":"Proceedings of the 3rd International Conference on Learning Representations"},{"article-title":"Tensorflow: large-scale machine learning on heterogeneous systems","year":"0","author":"abadi","key":"ref12"},{"key":"ref13","first-page":"165","article-title":"Label efficient learning of transferable representations across domains and tasks","volume":"30","author":"luo","year":"2017","journal-title":"Proc of the 30th Neural Information Processing Systems (NIPS’17)"},{"key":"ref14","article-title":"Mixup: beyond empirical risk minimization","author":"zhang","year":"2018","journal-title":"The 6th International Conference on Learning Representations (ICLR)"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00575"},{"key":"ref16","article-title":"Manifold mixup: encouraging meaningful on-manifold interpolation as a regularizer","author":"verma","year":"2018","journal-title":"arXiv 1806 05236"},{"key":"ref17","article-title":"Improved techniques for training gans","author":"salimans","year":"2016","journal-title":"arXiv 1606 03498"},{"key":"ref18","article-title":"Mode regularized generative adversarial networks","author":"che","year":"2017","journal-title":"Proc of the 5th International Conference on Learning Representations (ICLR’17)"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2343216"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995702"},{"key":"ref3","article-title":"Variational discriminator bottleneck: improving imitation learning, inverse rl, and gans by constraining information flow","author":"peng","year":"2019","journal-title":"Proceedings of the 7th International Conference on Learning Representations (ICLR)"},{"journal-title":"Keras","year":"0","author":"chollet","key":"ref6"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-26142-9_24"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1162\/089976600300015015"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref7","first-page":"115","article-title":"Learning precise timing with lstm recurrent networks","volume":"3","author":"gers","year":"2002","journal-title":"Journal of Machine Learning Research"},{"key":"ref2","first-page":"1397","article-title":"Fast generalization error bound of deep learning from a kernel perspective","volume":"84","author":"suzuki","year":"2018","journal-title":"Proc of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS’18)"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1137\/1.9781611972801.71","article-title":"Predictive modeling with heterogeneous sources","author":"shi","year":"2010","journal-title":"Proceedings of the 2010 SIAM International Conference on Data Mining"},{"key":"ref1","first-page":"1188","article-title":"Distributed representations of sentences and documents","volume":"32","author":"le","year":"2014","journal-title":"Proceedings of the 31st International Conference on Machine Learning"},{"key":"ref20","article-title":"Deep variational information bottleneck","author":"alemi","year":"2017","journal-title":"Proc of the 5th International Conference on Learning Representations (ICLR’17)"},{"key":"ref22","first-page":"24:594","article-title":"Relative density-ratio estimation for robust distribution comparison","author":"yamada","year":"2011","journal-title":"Proc of the 24th Neural Information Processing Systems (NIPS’11)"},{"key":"ref21","article-title":"Tutorial on variational autoencoders","author":"doersch","year":"2016","journal-title":"arXiv 1606 05908v2"},{"key":"ref24","first-page":"1785","article-title":"Heterogeneous domain transforms","volume":"23","author":"yeh","year":"2011","journal-title":"Proc of the IEEE Conference on Computer adaptation and classification by exploiting the correlation Vision and Pattern Recognition (CVPR’11)"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2010.65"},{"key":"ref26","first-page":"1541","article-title":"Heterogeneous domain adaptation using manifold alignment","author":"wang","year":"2011","journal-title":"Proc of the 22nd International Joint Conference on Artificial Intelligence (IJCAI’11)"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.167"}],"event":{"name":"2019 IEEE International Conference on Big Data (Big Data)","start":{"date-parts":[[2019,12,9]]},"location":"Los Angeles, CA, USA","end":{"date-parts":[[2019,12,12]]}},"container-title":["2019 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8986695\/9005444\/09006010.pdf?arnumber=9006010","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,17]],"date-time":"2022-07-17T17:50:58Z","timestamp":1658080258000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9006010\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":29,"URL":"https:\/\/doi.org\/10.1109\/bigdata47090.2019.9006010","relation":{},"subject":[],"published":{"date-parts":[[2019,12]]}}}