Quantized Variational Inference

• URL: http://arxiv.org/abs/2011.02271v1
• Date: Wed, 4 Nov 2020 13:22:50 GMT
• Title: Quantized Variational Inference
• Authors: Amir Dib
• Abstract summary: We show how Quantized Variational Inference produces variance free gradients for ELBO optimization. We show that using Quantized Variational Inference framework leads to fast convergence for both score function and reparametrized gradient.
• Score: 6.09170287691728
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Quantized Variational Inference, a new algorithm for Evidence Lower Bound maximization. We show how Optimal Voronoi Tesselation produces variance free gradients for ELBO optimization at the cost of introducing asymptotically decaying bias. Subsequently, we propose a Richardson extrapolation type method to improve the asymptotic bound. We show that using the Quantized Variational Inference framework leads to fast convergence for both score function and the reparametrized gradient estimator at a comparable computational cost. Finally, we propose several experiments to assess the performance of our method and its limitations.

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