{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T05:27:24Z","timestamp":1730266044679,"version":"3.28.0"},"reference-count":40,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,6,18]],"date-time":"2023-06-18T00:00:00Z","timestamp":1687046400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,6,18]],"date-time":"2023-06-18T00:00:00Z","timestamp":1687046400000},"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":[[2023,6,18]]},"DOI":"10.1109\/ijcnn54540.2023.10191415","type":"proceedings-article","created":{"date-parts":[[2023,8,2]],"date-time":"2023-08-02T17:30:03Z","timestamp":1690997403000},"page":"1-8","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Control-Variates Approach for Performative Gradient-Based Learners with Missing Data"],"prefix":"10.1109","author":[{"given":"Xing","family":"Han","sequence":"first","affiliation":[{"name":"UT-Austin,Austin,USA"}]},{"given":"Jing","family":"Hu","sequence":"additional","affiliation":[{"name":"Intuit,Mountain View,USA"}]},{"given":"Joydeep","family":"Ghosh","sequence":"additional","affiliation":[{"name":"UT-Austin,Austin,USA"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2014.6957805"},{"key":"ref35","volume":"86","author":"vach","year":"2012","journal-title":"Logistic Regression with Missing Values in the Covariates"},{"journal-title":"UCI Machine Learning Repository","year":"2017","author":"dua","key":"ref12"},{"key":"ref34","first-page":"2627","article-title":"Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models","author":"tucker","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1137\/120880811"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390294"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-009-0295-6"},{"journal-title":"Multivariate imputation by chained equations MICE v1 0 user's manual","year":"2000","author":"van buuren","key":"ref36"},{"key":"ref31","first-page":"3076","article-title":"Estimating individual treatment effect: generalization bounds and algorithms","author":"shalit","year":"0","journal-title":"International Conference on Machine Learning"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1201\/9781439821862"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2006.01.014"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btr597"},{"key":"ref10","first-page":"15883","article-title":"On inductive biases for heterogeneous treatment effect estimation","volume":"34","author":"curth","year":"2021","journal-title":"Advances in neural information processing systems"},{"key":"ref32","article-title":"Adapting neural networks for the estimation of treatment effects","volume":"32","author":"shi","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1509\/jmr.16.0163"},{"key":"ref1","first-page":"1031","article-title":"The costs of low birth weight","volume":"120","author":"almond","year":"2005","journal-title":"The Quarterly Journal of Economics"},{"key":"ref17","first-page":"1471","article-title":"Variance reduction techniques for gradient estimates in reinforcement learning","volume":"5","author":"greensmith","year":"2004","journal-title":"Journal of Machine Learning Research"},{"key":"ref39","first-page":"6638","article-title":"Doubly robust joint learning for recommendation on data missing not at random","author":"wang","year":"2019","journal-title":"International Conference on Machine Learning"},{"key":"ref16","article-title":"Multiple imputation using deep denoising autoencoders","author":"gondara","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref38","first-page":"181","article-title":"Variance reduction for stochastic gradient optimization","author":"wang","year":"2013","journal-title":"Advances in neural information processing systems"},{"journal-title":"Variance reduction three approaches to control variates","year":"2007","author":"lidebrandt","key":"ref19"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1198\/jcgs.2010.08162"},{"key":"ref24","first-page":"2287","article-title":"Spectral regularization algorithms for learning large incomplete matrices","volume":"11","author":"mazumder","year":"2010","journal-title":"Journal of Machine Learning Research"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3290607.3299041"},{"key":"ref26","first-page":"814","article-title":"Black box variational inference","author":"ranganath","year":"2014","journal-title":"Artificial Intelligence and Statistics"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511803161"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMsr1203730"},{"key":"ref22","article-title":"Black-box importancesampling","author":"liu","year":"2017","journal-title":"Artificial Intelligence and Statistics"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.2307\/2290664"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1108\/IJM-04-2019-0184"},{"key":"ref27","first-page":"1278","article-title":"Stochastic backpropagation and approximate inference in deep generative models","author":"rezende","year":"0","journal-title":"International Conference on Machine Learning"},{"key":"ref29","volume":"81","author":"rubin","year":"2004","journal-title":"Multiple Imputation for Nonresponse in Surveys"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-008-0135-7"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-24271-9"},{"key":"ref9","first-page":"1810","article-title":"Nonparametric estimation of heterogeneous treatment effects: From theory to learning algorithms","author":"curth","year":"0","journal-title":"International Conference on Artificial Intelligence and Statistics"},{"key":"ref4","article-title":"To impute or not to impute?-missing data in treatment effect estimation","author":"berrevoets","year":"2022","journal-title":"ArXiv Preprint"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-018-9826-2"},{"key":"ref6","first-page":"1","article-title":"mice: Multivariate imputation by chained equations in r","author":"van buuren","year":"2010","journal-title":"Journal of Statistical Software"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/0304-405X(77)90005-8"},{"key":"ref40","article-title":"Gain: Missing data imputation using generative adversarial nets","author":"yoon","year":"2018","journal-title":"ArXiv Preprint"}],"event":{"name":"2023 International Joint Conference on Neural Networks (IJCNN)","start":{"date-parts":[[2023,6,18]]},"location":"Gold Coast, Australia","end":{"date-parts":[[2023,6,23]]}},"container-title":["2023 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10190990\/10190992\/10191415.pdf?arnumber=10191415","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T17:42:38Z","timestamp":1692639758000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10191415\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,18]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/ijcnn54540.2023.10191415","relation":{},"subject":[],"published":{"date-parts":[[2023,6,18]]}}}