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This paper proposes a novel approach for constructing effective personalized policies when the observed data lacks counter-factual information, is biased and possesses many features.
Audibert JY, Munos R, Szepesvári C (2009) Exploration–exploitation tradeoff using variance estimates in multi-armed bandits. Theoretical Computer Science 410(19):1876–1902
1902
Earlier work this paper cites.
Prentice R (1976) Use of the logistic model in retrospective studies. Biometrics pp 599–606
1976
Earlier work this paper cites.
Rosenbaum PR, Rubin DB (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70(1):41–55
1983
Earlier work this paper cites.
Kira K, Rendell LA (1992) A practical approach to feature selection. In: Proceedings of the ninth international workshop on Machine learning, pp 249–256
1992
Earlier work this paper cites.
Williams RJ (1992) Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning pp 5–32
1992
Earlier work this paper cites.
Koller D, Sahami M (1996) Toward optimal feature selection
1996
Earlier work this paper cites.
Hall MA (1999) Correlation-based feature selection for machine learning. PhD thesis, The University of Waikato
1999
Earlier work this paper cites.
Weston J, Elisseeff A, Schölkopf B, Tipping M (2003) Use of the zero-norm with linear models and kernel methods. Journal of machine learning research 3:1439–1461
2003
Earlier work this paper cites.
Yu L, Liu H (2003) Feature selection for high-dimensional data: A fast correlation-based filter solution. In: International Conference on Machine Learning (ICML), vol 3, pp 856–863
2003
Earlier work this paper cites.
Robnik-Šikonja M, Kononenko I (2003) Theoretical and empirical analysis of relieff and rrelieff. Machine learning 53(1-2):23–69
2003
Earlier work this paper cites.
2003
Earlier work this paper cites.
Dy JG, Brodley CE (2004) Feature selection for unsupervised learning. Journal of machine learning research 5(845–889)
2004
Earlier work this paper cites.
He X, Cai D, Niyogi P (2005) Laplacian score for feature selection. In: Advances in neural information processing systems, pp 507–514
2005
Earlier work this paper cites.
Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. Pattern Analysis and Machine Intelligence, IEEE Transactions on 27(8):1226–1238
2005
Earlier work this paper cites.
Ionides EL (2008) Truncated importance sampling. Journal of Computational and Graphical Statistics 17(2):295–311
2008
Cited alongside, same era.
Beygelzimer, A, Langford, J (2009) The offset tree for learning with partial labels. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 129-138
2009
Cited alongside, same era.
Maurer A, Pontil M (2009) Empirical bernstein bounds and sample variance penalization. In: The 22nd Conference on Learning Theory
2009
Cited alongside, same era.
Strehl A, Langford J, Li L, Kakade SM (2010) Learning from logged implicit exploration data. In: Advances in Neural Information Processing Systems, pp 2217–2225
2010
Cited alongside, same era.
Xu Z, King I, Lyu MRT, Jin R (2010) Discriminative semi-supervised feature selection via manifold regularization. IEEE Transactions on Neural Networks 21(7):1033–1047
Athey S, Imbens GW (2015) Recursive partitioning for heterogeneous causal effects. arXiv preprint arXiv:150401132
2015
Later among the works it cites.
Swaminathan A, Joachims T (2015) Batch learning from logged bandit feedback through counterfactual risk minimization. Journal of Machine Learning Research 16:1731–1755
2015
Later among the works it cites.
Wager S, Athey S (2015) Estimation and inference of heterogeneous treatment effects using random forests. arXiv preprint arXiv:151004342
2015
Later among the works it cites.
Swaminathan A, Joachims T (2015) The self-normalized estimator for counterfactual learning. In: Advances in Neural Information Processing Systems, pp 3231–3239
2015
Later among the works it cites.
Hoiles W, van der Schaar M (2016) Bounded off-policy evaluation with missing data for course recommendation and curriculum design bounded off-policy evaluation with missing data for course recommendation and curriculum design. In: International Conference on Machine Learning, pp 1596–1604
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2010
Cited alongside, same era.
Dudík M, Langford J, Li L (2011) Doubly robust policy evaluation and learning. In: International Conference on Machine Learning (ICML)
2011
Cited alongside, same era.
Tian L, Alizadeh A, Gentles A, Tibshirani R (2012) A simple method for detecting interactions between a treatment and a large number of covariates. arXiv preprint arXiv:12122995
2012
Cited alongside, same era.
Song L, Smola A, Gretton A, Bedo J, Borgwardt K (2012) Feature selection via dependence maximization. Journal of Machine Learning Research 13(May):1393–1434
2012
Cited alongside, same era.
Duda RO, Hart PE, Stork DG (2012) Pattern classification. John Wiley & Sons
2012
Cited alongside, same era.
Bottou L, Peters J, Candela JQ, Charles DX, Chickering M, Portugaly E, Ray D, Simard PY, Snelson E (2013) Counterfactual reasoning and learning systems: the example of computational advertising. Journal of Machine Learning Research 14(1):3207–3260
2013
Cited alongside, same era.
Tekin C, van der Schaar M (2014) Discovering, learning and exploiting relevance. In: Advances in Neural Information Processing Systems, pp 1233–1241
2014
Cited alongside, same era.
Tang J, Alelyani S, Liu H (2014) Feature selection for classification: A review. Data Classification: Algorithms and Applications
2014
Cited alongside, same era.
2016
Closest in time.
Johansson F, Shalit U, Sontag D (2016) Learning representations for counterfactual inference. In: International Conference on Machine Learning (ICML)
2016
Closest in time.
Yoon J, Davtyan C, van der Schaar M (2016) Discovery and clinical decision support for personalized healthcare. IEEE journal of biomedical and health informatics
2016
Closest in time.
Shalit U, Johansson F, Sontag D (2016) Estimating individual treatment effect: generalization bounds and algorithms. arXiv preprint arXiv:160603976
2016
Closest in time.
Joachims T, Swaminathan A (2016) Counterfactual evaluation and learning for search, recommendation and ad placement. In: International ACM SIGIR conference on Research and Development in Information Retrieval, pp 1199–1201
2016
Closest in time.
Jiang N, Li L (2016) Doubly robust off-policy evaluation for reinforcement learning. In: International Conference on Machine Learning (ICML)
2016
Closest in time.
Alaa AM, van der Schaar M (2017) Bayesian inference of individualized treatment effects using multi-task gaussian processes. arXiv preprint arXiv:170402801
2017
Closest in time.
Joachims T, Grotov A, Swaminathan A, de Rijke M (2018) Deep learning with logged bandit feedback. In: International Conference on Learning Representations (ICLR)
2018
Closest in time.
Atan O, Zame WR, van der Schaar M (2018) Learning optimal policies from observational data. arXiv preprint arXiv:180208679
2018
Closest in time.