The generalized reparameterization gradient

Advances in Neural Information Processing Systems

Volumn 0, Issue , 2016, Pages 460-468

  Ruiz, Francisco J R ^a,b   Titsias, Michalis K ^c   Blei, David M ^b  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b Och Spine at New York Presbyterian Hospitals   (United States)

^c ATHENS UNIVERSITY OF ECONOMICS AND BUSINESS   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

LATENT VARIABLE; MONTE CARLO GRADIENTS; PROBABILISTIC MODELS; REPARAMETERIZATION; SCORE FUNCTION; SINGLE SAMPLE; VARIATIONAL INFERENCE; VARIATIONAL PARAMETERS;

MONTE CARLO METHODS;

EID: 85016392055     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (34)

1. Baydin, A. G., Pearlmutter, B. A., and Radul, A. A. (2015). Automatic differentiation in machine learning: a survey. arXiv:1502.05767.
2. Bonnet, G. (1964). Transformations des signaux aléatoires a travers les systemes non linéaires sans mémoire. Annals of Telecommunications, 19(9):203-220.
3. Carbonetto, P., King, M., and Hamze, F. (2009). A stochastic approximation method for inference in probabilistic graphical models. In Advances in Neural Information Processing Systems.
4. Casella, G. and Robert, C. P. (1996). Rao-Blackwellisation of sampling schemes. Biometrika, 83(1):81-94.
5. Duchi, J., Hazan, E., and Singer, Y. (2011). Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 12:2121-2159.
6. Fan, K., Wang, Z., Beck, J., Kwok, J., and Heller, K. A. (2015). Fast second order stochastic backpropagation for variational inference. In Advances in Neural Information Processing Systems.
7. Ghahramani, Z. and Beal, M. J. (2001). Propagation algorithms for variational Bayesian learning. In Advances in Neural Information Processing Systems.
8. Glynn, P. W. (1990). Likelihood ratio gradient estimation for stochastic systems. Communications of the ACM, 33(10):75-84.
9. Gu, S., Levine, S., Sutskever, I., and Mnih, A. (2016). MuProp: Unbiased backpropagation for stochastic neural networks. In International Conference on Learning Representations.
10. Hinton, G., Dayan, P., Frey, B. J., and Neal, R. M. (1995). The wake-sleep algorithm for unsupervised neural networks. Science, 268(5214):1158-1161.
11. Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., and Saul, L. K. (1999). An introduction to variational methods for graphical models. Machine Learning, 37(2):183-233.
12. Kingma, D. P. and Welling, M. (2014). Auto-encoding variational Bayes. In International Conference on Learning Representations.
13. Knowles, D. A. (2015). Stochastic gradient variational Bayes for gamma approximating distributions. arXiv:1509.01631v1.
14. Kucukelbir, A., Ranganath, R., Gelman, A., and Blei, D. M. (2015). Automatic variational inference in Stan. In Advances in Neural Information Processing Systems.
15. Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., and Blei, D. M. (2016). Automatic differentiation variational inference. arXiv:1603.00788.
16. Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266):1332-1338.
17. Mnih, A. and Gregor, K. (2014). Neural variational inference and learning in belief networks. In International Conference on Machine Learning.
18. Neal, R. (1992). Connectionist learning of belief networks. Artificial Intelligence, 56(1):71-113.
19. Paisley, J. W., Blei, D. M., and Jordan, M. I. (2012). Variational Bayesian inference with stochastic search. In International Conference on Machine Learning.
20. Price, R. (1958). A useful theorem for nonlinear devices having Gaussian inputs. IRE Transactions on Information Theory, 4(2):69-72.
21. Ranganath, R., Gerrish, S., and Blei, D. M. (2014). Black box variational inference. In Artificial Intelligence and Statistics.
22. Ranganath, R., Tang, L., Charlin, L., and Blei, D. M. (2015). Deep exponential families. In Artificial Intelligence and Statistics.
23. Rezende, D. J., Mohamed, S., and Wierstra, D. (2014). Stochastic backpropagation and approximate inference in deep generative models. In International Conference on Machine Learning.
24. Ross, S. M. (2002). Simulation. Elsevier.
25. Ruiz, F. J. R., Titsias, M. K., and Blei, D. M. (2016). Overdispersed black-box variational inference. In Uncertainty in Artificial Intelligence.
26. Salimans, T. and Knowles, D. A. (2013). Fixed-form variational posterior approximation through stochastic linear regression. Bayesian Analysis, 8(4):837-882.
27. Schulman, J., Heess, N., Weber, T., and Abbeel, P. (2015). Gradient estimation using stochastic computation graphs. In Advances in Neural Information Processing Systems.
28. Tieleman, T. and Hinton, G. (2012). Lecture 6.5-RMSPROP: Divide the gradient by a running average of its recent magnitude. Coursera: Neural Networks for Machine Learning, 4.
29. Titsias, M. K. and Lázaro-Gredilla, M. (2014). Doubly stochastic variational Bayes for non-conjugate inference. In International Conference on Machine Learning.
30. Titsias, M. K. and Lázaro-Gredilla, M. (2015). Local expectation gradients for black box variational inference. In Advances in Neural Information Processing Systems.
31. van de Meent, J.-W., Tolpin, D., Paige, B., and Wood, F. (2016). Black-box policy search with probabilistic programs. In Artificial Intelligence and Statistics.
32. Wainwright, M. J. and Jordan, M. I. (2008). Graphical models, exponential families, and variational inference. Foundations and Trends in Machine Learning, 1(1-2):1-305.
33. Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3-4):229-256.
34. Wingate, D. and Weber, T. (2013). Automated variational inference in probabilistic programming. arXiv:1301.1299.