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A Gentle Introduction to Recommendation as Counterfactual Policy Learning.

, , , and . UMAP, page 392-393. ACM, (2020)

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MCMC methods for univariate exponential family models with intractable normalization constants., and . SSP, page 356-359. IEEE, (2014)Graphical tools for conditional probabilistic exploration of multivariate spatial datasets., and . Comput. Environ. Urban Syst., 36 (5): 359-370 (2012)A Gentle Introduction to Recommendation as Counterfactual Policy Learning., , , and . UMAP, page 392-393. ACM, (2020)Machine Learning for Matching Astronomy Catalogues., , , , and . IDEAL, volume 3177 of Lecture Notes in Computer Science, page 702-707. Springer, (2004)Semiparametric Mean Field Variational Bayes: General Principles and Numerical Issues., and . J. Mach. Learn. Res., (2016)Three Methods for Training on Bandit Feedback., , and . CoRR, (2019)Causal Inference, is just Inference: A beautifully simple idea that not everyone accepts.. ICBINB@NeurIPS, volume 163 of Proceedings of Machine Learning Research, page 75-79. PMLR, (2021)Learning from Bandit Feedback: An Overview of the State-of-the-art., , , , , and . CoRR, (2019)Reconsidering Analytical Variational Bounds for Output Layers of Deep Networks., , , and . CoRR, (2019)On the Value of Bandit Feedback for Offline Recommender System Evaluation., , and . CoRR, (2019)