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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.
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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.
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ABC and likelihood-free inference
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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.
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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.
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Applications
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