Probabilistic Programming

Lectures

Link to source Google sheet.

Suggested Reading

  1. D. P. Kingma and M. Welling, “Auto-encoding variational Bayes,” in International Conference on Learning Representations, 2014.
  2. D. J. Rezende, S. Mohamed, and D. Wierstra, “Stochastic backpropagation and approximate inference in deep generative models,” in International Conference on Machine Learning, 2014.
  3. P. Dayan, G. E. Hinton, R. M. Neal, and R. S. Zemel, “The Helmholtz machine,” Neural computation, vol. 7, no. 5, pp. 889–904, 1995.
  4. N. Siddharth et al., “Learning disentangled representations with semi-supervised deep generative models,” in Advances in Neural Information Processing Systems, 2017, pp. 5925–5935.
  5. D. Tran, M. D. Hoffman, R. A. Saurous, E. Brevdo, K. Murphy, and D. M. Blei, “Deep probabilistic programming,” arXiv preprint arXiv:1701.03757, 2017.
  6. 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.
  7. J. Bornschein and Y. Bengio, “Reweighted Wake-Sleep,” in International Conference on Learning Representations, 2015.
  8. T. A. Le, A. R. Kosiorek, N. Siddharth, Y. W. Teh, and F. Wood, “Revisiting Reweighted Wake-Sleep,” arXiv preprint arXiv:1805.10469, 2018.
  9. 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.
  10. S. Levine, “Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review,” arXiv preprint arXiv:1805.00909, 2018.
  11. L. Ouyang, M. H. Tessler, D. Ly, and N. Goodman, “Practical optimal experiment design with probabilistic programs,” arXiv preprint arXiv:1608.05046, 2016.
  12. D. J. Rezende and S. Mohamed, “Variational inference with normalizing flows,” arXiv preprint arXiv:1505.05770, 2015.
  13. A. Doucet and A. M. Johansen, “A tutorial on particle filtering and smoothing: Fifteen years later,” Handbook of Nonlinear Filtering, vol. 12, no. 656–704, p. 3, 2009.
  14. B. Paige, F. Wood, A. Doucet, and Y. W. Teh, “Asynchronous anytime sequential monte carlo,” in Advances in Neural Information Processing Systems, 2014, pp. 3410–3418.
  15. T. Rainforth et al., “Interacting Particle Markov Chain Monte Carlo,” in International Conference on Machine Learning, 2016, pp. 2616–2625.
  16. N. D. Goodman, V. K. Mansinghka, D. Roy, K. Bonawitz, and J. B. Tenenbaum, “Church: a language for generative models,” in Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence, 2008, pp. 220–229.
  17. 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.
  18. F. Wood, J. W. van de Meent, and V. Mansinghka, “A New Approach to Probabilistic Programming Inference,” ArXiv e-prints, Jul. 2015.
  19. 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.
  20. 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.
  21. 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.
  22. A. Griewank and A. Walther, Evaluating derivatives: principles and techniques of algorithmic differentiation, vol. 105. Siam, 2008.
  23. T. A. Le, M. Igl, T. Rainforth, T. Jin, and F. Wood, “Auto-Encoding Sequential Monte Carlo,” in International Conference on Learning Representations, 2018.
  24. C. J. Maddison et al., “Filtering Variational Objectives,” in Advances in Neural Information Processing Systems, 2017, pp. 6576–6586.
  25. C. Naesseth, S. Linderman, R. Ranganath, and D. Blei, “Variational Sequential Monte Carlo,” in International Conference on Artificial Intelligence and Statistics, 2018.