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Akash Kumar Dhaka

2 accepted papers

2021

Challenges and Opportunities in High Dimensional Variational Inference

NeurIPS 2021poster

Current black-box variational inference (BBVI) methods require the user to make numerous design choices – such as the selection of variational objective and approximating family – yet there is little principled guidance on how to do so. We develop a conceptual framework and set of experimental tools…

Cited by 57Source ↗PDF ↗Scholar ↗
2020

Robust, Accurate Stochastic Optimization for Variational Inference

NeurIPS 2020poster

We examine the accuracy of black box variational posterior approximations for parametric models in a probabilistic programming context. The performance of these approximations depends on (1) how well the variational family approximates the true posterior distribution, (2) the choice of divergence, a…

Cited by 42Source ↗PDF ↗Scholar ↗