A Hybrid Approach for Probabilistic Inference using Random Projections


Michael Zhu, Stefano Ermon ;
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2039-2047, 2015.


We introduce a new meta-algorithm for probabilistic inference in graphical models based on random projections. The key idea is to use approximate inference algorithms for an (exponentially) large number of samples, obtained by randomly projecting the original statistical model using universal hash functions. In the case where the approximate inference algorithm is a variational approximation, this approach can be viewed as interpolating between sampling-based and variational techniques. The number of samples used controls the trade-off between the accuracy of the approximate inference algorithm and the variance of the estimator. We show empirically that by using random projections, we can improve the accuracy of common approximate inference algorithms.

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