Bibliography

Implicit Models

  • Diggle, P. J., & Gratton, R. J. (1984). Monte Carlo methods of inference for implicit statistical models. Journal of the Royal Statistical Society Series B.
  • Hartig, F., Calabrese, J. M., Reineking, B., Wiegand, T., & Huth, A. (2011). Statistical inference for stochastic simulation models - theory and application. Ecology Letters, 14(8), 816–827.
  • Mohamed, S., & Lakshminarayanan, B. (2016). Learning in Implicit Generative Models. arXiv preprint arXiv:1610.03483.
  • Tran, D., Ranganath, R., & Blei, D. M. (2017). Deep and Hierarchical Implicit Models. arXiv preprint arXiv:1702.08896.

Adversarial Training

  • Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., et al. (2014). Generative Adversarial Nets. In Neural Information Processing Systems.
  • Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434.
  • Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016). Improved Techniques for Training GANs. In Neural Information Processing Systems.
  • Nowozin, S., Cseke, B., & Tomioka, R. (2016). f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization. In Neural Information Processing Systems.
  • Arora, S., Ge, R., Liang, Y., Ma, & T., Zhang, Y. (2017) Generalization and Equilibrium in Generative Adversarial Nets (GANs). arXiv preprint arXiv:1703.00573.
  • Arjovsky, M., & Bottou, L. (2017). Towards Principled Methods for Training Generative Adversarial Networks. In International Conference on Learning Representations.
  • Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., & Abbeel, P. (2016). InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. In Neural Information Processing Systems.
  • Dziugaite, G. K., Roy, D. M., & Ghahramani, Z. (2015). Training generative neural networks via Maximum Mean Discrepancy optimization. In Uncertainty in Artificial Intelligence.
  • Li, Y., Swersky, K., & Zemel, R. S. (2015). Generative Moment Matching Networks. In ICML (pp. 1718-1727).

Variational Inference

  • Huszár, F. (2017). Variational Inference using Implicit Distributions. arXiv preprint arXiv:1702.08235.
  • Karaletsos, T. (2016). Adversarial Message Passing For Graphical Models. arXiv preprint arXiv:1612.05048.
  • Mescheder, L., Nowozin, S., & Geiger, A. (2017). Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks. arXiv preprint arXiv:1701.04722.
  • Ranganath, R., Tran, D., & Blei, D. M. (2016). Hierarchical Variational Models. In International Conference on Machine Learning.
  • Ranganath, R., Altosaar, J., Tran, D., & Blei, D. M. (2016). Operator Variational Inference. In Neural Information Processing Systems.
  • Wang, D., & Liu, Q. (2016). Learning to draw samples: With application to amortized MLE for generative adversarial learning. arXiv preprint arXiv:1611.01722.

ABC and likelihood-free inference

  • Gourieroux, C., Monfort, A., & Renault, E. (1993). Indirect inference. Journal of Applied Econometrics, 8(S1), S85–S118.
  • Gutmann, M. U., & Corander, J. (2015). Bayesian optimization for likelihood-free inference of simulator-based statistical models. Journal of Machine Learning Research.
  • Gutmann, M. U., Dutta, R., Kaski, S., & Corander, J. (2017). Statistical Inference of Intractable Generative Models via Classification. Statistics and Computing.
  • Lintusaari, J., Gutmann, M. U., Dutta, R., Kaski, S., & Corander, J. (2016). Fundamentals and recent developments in approximate Bayesian computation. Systematic Biology, syw077.
  • Marjoram, P., Molitor, J., Plagnol, V., & Tavaré, S. (2003). Markov chain Monte Carlo without likelihoods. Proceedings of the National Academy of Sciences, 100(26), 15324–15328.
  • Meeds, E., Leenders, R., & Welling, M. (2015). Hamiltonian ABC. arXiv preprint arXiv:1503.01916.
  • Rubin, D. B. (1984). Bayesianly justifiable and relevant frequency calculations for the applied statistician. The Annals of Statistics, 12(4), 1151–1172.
  • Sisson, S. A., Fan, Y., & Tanaka, M. M. (2009). Sequential Monte Carlo without likelihoods. Proceedings of the National Academy of Sciences.
  • Tran, M., Nott, D., & Kohn, R. (2015). Variational Bayes with intractable likelihood. arXiv preprint arXiv:1503.08621.
  • Wood, S. N. (2010). Statistical inference for noisy nonlinear ecological dynamic systems. Nature, 466(7310), 1102–1104.

Two Sample Testing & Density Ratio Estimation

  • Cranmer, K., Pavez, J., & Louppe, G. (2015). Approximating Likelihood Ratios with Calibrated Discriminative Classifiers. arXiv preprint arXiv:1506.02169.
  • Uehara, M., Sato, I., Suzuki, M., Nakayama, K. and Mat- suo, Y. Generative adversarial nets from a density ratio estimation perspective. arXiv preprint arXiv:1610.02920, 2016.
  • Sugiyama, M., Suzuki, T., & Kanamori, T. (2012). Density-ratio matching under the Bregman divergence: A unified framework of density-ratio estimation. Annals of the Institute of Statistical Mathematics
  • Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., & Smola, A. (2012). A kernel two-sample test. The Journal of Machine Learning Research, 13, 723–773.
  • Lopez-Paz, D., & Oquab, M. (2016). Revisiting Classifier Two-Sample Tests. arXiv preprint arXiv:1610.06545.

Evaluation & Model criticism

  • Pudlo, P., Marin, J. M., Estoup, A., Cornuet, J. M., Gautier, M., & Robert, C. P. (2015). Reliable ABC model choice via random forests. Bioinformatics.
  • Theis, L., Oord, A. V. D., & Bethge, M. (2015). A note on the evaluation of generative models. arXiv preprint arXiv:1511.01844.
  • Wu, Y., Burda, Y., Salakhutdinov, R., & Grosse, R. (2016). On the Quantitative Analysis of Decoder-Based Generative Models. arXiv preprint arXiv:1611.04273.
  • Sutherland, D. J., Tung, H. Y., Strathmann, H., De, S., Ramdas, A., Smola, A., & Gretton, A. (2016). Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy. arXiv preprint arXiv:1611.04488.

Applications

  • Beaumont, M., Zhang, W., & Balding, D. (2002). Approximate Bayesian Computation in Population Genetics. Genetics.
  • Beaumont, M. (2010). Approximate Bayesian Computation in Evolution and Ecology. Annual Review of Ecology, Evolution, and Systematics
  • Li, J., Monroe, W., Shi, T., Ritter, A., & Jurafsky, D. (2017). Adversarial Learning for Neural Dialogue Generation. arXiv preprint arXiv:1701.06547.
  • Ho, J., & Ermon, S. (2016). Generative adversarial imitation learning. In Advances in Neural Information Processing Systems (pp. 4565-4573).
  • Pearl, J. (2000). Causality. Cambridge University Press.
  • Tavaré, S., Balding, D. J., Griffiths, R. C., & Donnelly, P. (1997). Inferring Coalescence Times From DNA Sequence. Genetics, 145(2), 505–518.
  • Tanaka, M., Francis, A., Luciani, F., & Sisson, S. (2006). Using Approximate Bayesian Computation to Estimate Tuberculosis Transmission Parameters From Genotype Data. Genetics.