Multi-source domain adaptation: a causal view
作者： Bernhard Scholkopf , Mingming Gong , Kun Zhang
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摘要: … This paper is concerned with the problem of domain adaptation with multiple sources from a causal point of view. In particular, we use causal models to represent the relationship …
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acm.org 参考文章(36)
Elias Bareinboim, Judea Pearl, Jin Tian, Recovering from selection bias in causal and statistical inference national conference on artificial intelligence. pp. 2410- 2416 ,(2014)
Motoaki Kawanabe, Masashi Sugiyama, Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation ,(2012)
Judea Pearl, Jin Tian, Causal discovery from changes uncertainty in artificial intelligence. pp. 512- 521 ,(2001)
Janez Demšar, Statistical Comparisons of Classifiers over Multiple Data Sets Journal of Machine Learning Research. ,vol. 7, pp. 1- 30 ,(2006)
Pietro Perona, Gregory Griffin, Alex Holub, Caltech-256 Object Category Dataset California Institute of Technology. ,(2007)
Alex Smola, Arthur Gretton, Le Song, Bernhard Schölkopf, A Hilbert Space Embedding for Distributions Lecture Notes in Computer Science. pp. 13- 31 ,(2007) , 10.1007/978-3-540-75225-7_5
Rita Chattopadhyay, Qian Sun, Wei Fan, Ian Davidson, Sethuraman Panchanathan, Jieping Ye, Multi-source domain adaptation and its application to early detection of fatigue Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '11. pp. 717- 725 ,(2011) , 10.1145/2020408.2020520
Bianca Zadrozny, Learning and evaluating classifiers under sample selection bias Twenty-first international conference on Machine learning - ICML '04. pp. 114- ,(2004) , 10.1145/1015330.1015425
Hidetoshi Shimodaira, Improving predictive inference under covariate shift by weighting the log-likelihood function Journal of Statistical Planning and Inference. ,vol. 90, pp. 227- 244 ,(2000) , 10.1016/S0378-3758(00)00115-4
Jing Gao, Wei Fan, Jing Jiang, Jiawei Han, Knowledge transfer via multiple model local structure mapping Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 08. pp. 283- 291 ,(2008) , 10.1145/1401890.1401928