Link to source Google sheet.
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- J. Schulman, N. Heess, T. Weber, and P. Abbeel, “Gradient estimation using stochastic computation graphs,” in Advances in Neural Information Processing Systems, 2015, pp. 3528–3536.
- J. Bornschein and Y. Bengio, “Reweighted Wake-Sleep,” in International Conference on Learning Representations, 2015.
- T. A. Le, A. R. Kosiorek, N. Siddharth, Y. W. Teh, and F. Wood, “Revisiting Reweighted Wake-Sleep,” arXiv preprint arXiv:1805.10469, 2018.
- G. Tucker, A. Mnih, C. J. Maddison, J. Lawson, and J. Sohl-Dickstein, “Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models,” in Advances in Neural Information Processing Systems, 2017, pp. 2627–2636.
- S. Levine, “Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review,” arXiv preprint arXiv:1805.00909, 2018.
- L. Ouyang, M. H. Tessler, D. Ly, and N. Goodman, “Practical optimal experiment design with probabilistic programs,” arXiv preprint arXiv:1608.05046, 2016.
- D. J. Rezende and S. Mohamed, “Variational inference with normalizing flows,” arXiv preprint arXiv:1505.05770, 2015.
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- T. Rainforth et al., “Interacting Particle Markov Chain Monte Carlo,” in International Conference on Machine Learning, 2016, pp. 2616–2625.
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- D. Tolpin, J. W. van de Meent, H. Yang, and F. Wood, “Design and Implementation of Probabilistic Programming Language Anglican,” arXiv preprint arXiv:1608.05263, 2016.
- F. Wood, J. W. van de Meent, and V. Mansinghka, “A New Approach to Probabilistic Programming Inference,” ArXiv e-prints, Jul. 2015.
- D. Ritchie, A. Stuhlmüller, and N. Goodman, “C3: Lightweight incrementalized MCMC for probabilistic programs using continuations and callsite caching,” in Artificial Intelligence and Statistics, 2016, pp. 28–37.
- D. Wingate, A. Stuhlmüller, and N. Goodman, “Lightweight implementations of probabilistic programming languages via transformational compilation,” in Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011, pp. 770–778.
- J. W. van de Meent, B. Paige, H. Yang, and F. Wood, “Introduction to Probabilistic Programming,” Foundations and Trends in Machine Learning, pp. in review, 2018.
- A. Griewank and A. Walther, Evaluating derivatives: principles and techniques of algorithmic differentiation, vol. 105. Siam, 2008.
- T. A. Le, M. Igl, T. Rainforth, T. Jin, and F. Wood, “Auto-Encoding Sequential Monte Carlo,” in International Conference on Learning Representations, 2018.
- C. J. Maddison et al., “Filtering Variational Objectives,” in Advances in Neural Information Processing Systems, 2017, pp. 6576–6586.
- C. Naesseth, S. Linderman, R. Ranganath, and D. Blei, “Variational Sequential Monte Carlo,” in International Conference on Artificial Intelligence and Statistics, 2018.