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7 papers · 2 filters
On the Convergence of Bound Optimization Algorithms
Ruslan R Salakhutdinov, Sam T Roweis, Zoubin Ghahramani
Many practitioners who use the EM algorithm complain that it is sometimes slow. When does this happen, and what can be done about it? In this paper, we study the general class of b…
Hilbert Space Embeddings of POMDPs
Yu Nishiyama, Abdeslam Boularias, Arthur Gretton +1
A nonparametric approach for policy learning for POMDPs is proposed. The approach represents distributions over the states, observations, and actions as embeddings in feature space…
A Spectral Algorithm for Latent Junction Trees
Ankur P. Parikh, Le Song, Mariya Ishteva +2
Latent variable models are an elegant framework for capturing rich probabilistic dependencies in many applications. However, current approaches typically parametrize these models u…
Variable noise and dimensionality reduction for sparse Gaussian processes
Edward Snelson, Zoubin Ghahramani
The sparse pseudo-input Gaussian process (SPGP) is a new approximation method for speeding up GP regression in the case of a large number of data points N. The approximation is con…
Variational Inference in Non-negative Factorial Hidden Markov Models for Efficient Audio Source Separation
Gautham Mysore, Maneesh Sahani
The past decade has seen substantial work on the use of non-negative matrix factorization and its probabilistic counterparts for audio source separation. Although able to capture a…
Hybrid Variational/Gibbs Collapsed Inference in Topic Models
Max Welling, Yee Whye Teh, Hilbert Kappen
Variational Bayesian inference and (collapsed) Gibbs sampling are the two important classes of inference algorithms for Bayesian networks. Both have their advantages and disadvanta…