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Title: Effective Monte Carlo Variational Inference for Binary-Variable Probabilistic Programs
We propose a broadly applicable variational inference algorithm for probabilistic models with binary latent variables, using sampling to approximate expectations required for coordinate ascent updates. Applied to three real-world models for text and image and network data, our approach converges much faster than REINFORCE-style stochastic gradient algorithms, and requires fewer Monte Carlo samples. Compared to hand-crafted variational bounds with model-dependent auxiliary variables, our approach leads to tighter likelihood bounds and greater robustness to local optima. Our method is designed to integrate easily with probabilistic programming languages for effective, scalable, black-box variational inference.  more » « less
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International Conference on Probabilistic Programming
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National Science Foundation
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