Quantized Variational Inference

Part of Advances in Neural Information Processing Systems 33 (NeurIPS 2020)

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Amir Dib


We present Quantized Variational Inference, a new algorithm for Evidence Lower Bound minimization. We show how Optimal Voronoi Tesselation produces variance free gradients for Evidence Lower Bound (ELBO) optimization at the cost of introducing asymptotically decaying bias. Subsequently, we propose a Richardson extrapolation type method to improve this 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.