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arXiv · 1905.07855

MaxEntropy Pursuit Variational Inference

Evgenii Egorov·Kirill Neklydov·Ruslan Kostoev·Evgeny Burnaev
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Abstract

One of the core problems in variational inference is a choice of approximate posterior distribution. It is crucial to trade-off between efficient inference with simple families as mean-field models and accuracy of inference. We propose a variant of a greedy approximation of the posterior distribution with tractable base learners. Using Max-Entropy approach, we obtain a well-defined optimization problem. We demonstrate the ability of the method to capture complex multimodal posterior via continual learning setting for neural networks.

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Evgenii Egorov, Kirill Neklydov, Ruslan Kostoev, Evgeny Burnaev. 2019-05-20. MaxEntropy Pursuit Variational Inference. https://arxiv.org/abs/1905.07855

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