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Counterfactual reasoning and learning systems: The example of computational advertising . The Journal of Machine Learning Research14 , 1 (2013), 3207 -- 3260 . L. Bottou, J. Peters, J. Qui\u00f1onero-Candela, D. Charles, D. Chickering, E. Portugaly, D. Ray, P. Simard, and E. Snelson. 2013. Counterfactual reasoning and learning systems: The example of computational advertising. The Journal of Machine Learning Research14, 1 (2013), 3207--3260.","journal-title":"The Journal of Machine Learning Research14"},{"key":"e_1_3_2_1_7_1","unstructured":"Guillaume Bouchard. 2007. Efficient bounds for the softmax function applications to inference in hybrid models. (2007). Guillaume Bouchard. 2007. Efficient bounds for the softmax function applications to inference in hybrid models. 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In Proceedings of the fourth ACM conference on Recommender systems. 293--296."},{"key":"e_1_3_2_1_11_1","first-page":"518","article-title":"Similarity search in high dimensions via hashing","volume":"99","author":"Gionis Aristides","year":"1999","unstructured":"Aristides Gionis , Piotr Indyk , Rajeev Motwani , 1999 . Similarity search in high dimensions via hashing . In Vldb , Vol. 99. 518 -- 529 . Aristides Gionis, Piotr Indyk, Rajeev Motwani, et al.1999. Similarity search in high dimensions via hashing. In Vldb, Vol. 99. 518--529.","journal-title":"Vldb"},{"key":"e_1_3_2_1_12_1","unstructured":"Miguel A Hernan and James M Robins. 2010. Causal inference. (2010). Miguel A Hernan and James M Robins. 2010. Causal inference. (2010)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271761"},{"key":"e_1_3_2_1_14_1","volume-title":"Sixth International Workshop on Artificial Intelligence and Statistics","volume":"82","author":"Jaakkola Tommi","year":"1997","unstructured":"Tommi Jaakkola and Michael Jordan . 1997 . A variational approach to Bayesian logistic regression models and their extensions . In Sixth International Workshop on Artificial Intelligence and Statistics , Vol. 82 . 4. Tommi Jaakkola and Michael Jordan. 1997. A variational approach to Bayesian logistic regression models and their extensions. In Sixth International Workshop on Artificial Intelligence and Statistics, Vol. 82. 4."},{"key":"e_1_3_2_1_15_1","volume-title":"Proc. of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '20)","author":"Jeunen O.","unstructured":"O. Jeunen , D. Rohde , F. Vasile , and M. Bompaire . 2020. Joint Policy-Value Learning for Recommendation . 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In Advances in Neural Information Processing Systems. 2575--2583. Diederik P Kingma Tim Salimans and Max Welling. 2015. Variational dropout and the local reparameterization trick. In Advances in Neural Information Processing Systems. 2575--2583."},{"key":"e_1_3_2_1_18_1","volume-title":"Kingma and Max Welling","author":"Diederik","year":"2014","unstructured":"Diederik P. Kingma and Max Welling . 2014 . Auto-Encoding Variational Bayes. In2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14--16, 2014, Conference Track Proceedings, Yoshua Bengio and Yann Le Cun (Eds .). https:\/\/openreview.net\/group?id=ICLR.cc\/2014 Diederik P. Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes. In2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14--16, 2014, Conference Track Proceedings, Yoshua Bengio and Yann Le Cun (Eds.). https:\/\/openreview.net\/group?id=ICLR.cc\/2014"},{"key":"e_1_3_2_1_19_1","volume-title":"Knowles and Tom Minka","author":"David","year":"2011","unstructured":"David A. Knowles and Tom Minka . 2011 . Non-conjugate Variational Message Passing for Multinomial and Binary Regression. In Advances in Neural Information Processing Systems 24, J. Shawe-Taylor, R. S. Zemel, P. L. Bartlett, F. Pereira, and K. Q. Weinberger (Eds.). Curran Associates, Inc ., 1701--1709. David A. Knowles and Tom Minka. 2011. Non-conjugate Variational Message Passing for Multinomial and Binary Regression. In Advances in Neural Information Processing Systems 24, J. Shawe-Taylor, R. S. Zemel, P. L. Bartlett, F. Pereira, and K. Q. Weinberger (Eds.). 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Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. InAdvances in Neural Information Processing Systems 26, C. J. C. Burges, L. Bottou,M. Welling, Z. Ghahramani, and K. Q. Weinberger (Eds.). Curran Associates, Inc.,3111--3119."},{"key":"e_1_3_2_1_28_1","volume-title":"Bayesian learning for neural networks","author":"Neal Radford M","unstructured":"Radford M Neal . 2012. Bayesian learning for neural networks . Vol. 118 . Springer Science & Business Media . Radford M Neal. 2012. Bayesian learning for neural networks. Vol. 118. Springer Science & Business Media."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1002\/sta4.139"},{"key":"e_1_3_2_1_30_1","unstructured":"Adam Paszke Sam Gross Soumith Chintala Gregory Chanan Edward Yang Zachary DeVito Zeming Lin Alban Desmaison Luca Antiga and Adam Lerer.2017. Automatic differentiation in PyTorch. In NIPS-W. Adam Paszke Sam Gross Soumith Chintala Gregory Chanan Edward Yang Zachary DeVito Zeming Lin Alban Desmaison Luca Antiga and Adam Lerer.2017. Automatic differentiation in PyTorch. In NIPS-W."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1214\/14-STS483"},{"key":"e_1_3_2_1_32_1","volume-title":"A stochastic approximation method. The annals of mathematical statistics 22, 3","author":"Robbins Herbert","year":"1951","unstructured":"Herbert Robbins and Sutton Monro . 1951. A stochastic approximation method. The annals of mathematical statistics 22, 3 ( 1951 ), 400--407. Herbert Robbins and Sutton Monro. 1951. A stochastic approximation method. The annals of mathematical statistics 22, 3 (1951), 400--407."},{"key":"e_1_3_2_1_33_1","volume-title":"REVEAL workshop, ACM Conference on Recommender Systems","author":"Rohde David","year":"2018","unstructured":"David Rohde , Stephen Bonner , Travis Dunlop , Flavian Vasile , and Alexandros Karatzoglou . 2018 . 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The Journal of Machine LearningResearch17, 1 (2016), 5975--6021.","journal-title":"The Journal of Machine LearningResearch17"},{"key":"e_1_3_2_1_35_1","unstructured":"Francisco JR Ruiz Michalis K Titsias Adji B Dieng and David M Blei. 2018. Augment and reduce: Stochastic inference for large categorical distributions. arXiv preprint arXiv:1802.04220(2018). Francisco JR Ruiz Michalis K Titsias Adji B Dieng and David M Blei. 2018. Augment and reduce: Stochastic inference for large categorical distributions. arXiv preprint arXiv:1802.04220(2018)."},{"key":"e_1_3_2_1_36_1","volume-title":"Proceedings of the 33rd International Conference on International Conference on Machine Learning -","volume":"48","author":"Schnabel Tobias","year":"2016","unstructured":"Tobias Schnabel , Adith Swaminathan , Ashudeep Singh , Navin Chandak , and Thorsten Joachims . 2016 . Recommendations As Treatments: Debiasing Learning and Evaluation . In Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48 (ICML'16). 1670--1679. Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. 2016. Recommendations As Treatments: Debiasing Learning and Evaluation. In Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48 (ICML'16). 1670--1679."},{"key":"e_1_3_2_1_37_1","unstructured":"A. Storkey. 2009. When training and test sets are different: characterizing learning transfer. Dataset shift in machine learning(2009) 3--28. A. Storkey. 2009. When training and test sets are different: characterizing learning transfer. Dataset shift in machine learning(2009) 3--28."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.5555\/2789272.2886805"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.5555\/2789272.2886805"},{"key":"e_1_3_2_1_40_1","unstructured":"Michalis Titsias. 2016. One-vs-each approximation to softmax for scalable estimation of probabilities. In Advances in Neural Information Processing Systems. 4161--4169. Michalis Titsias. 2016. One-vs-each approximation to softmax for scalable estimation of probabilities. 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