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Found 3 papers, 1 papers with code
On the Convergence of Black-Box Variational Inference
We provide the first convergence guarantee for full black-box variational inference (BBVI), also known as Monte Carlo variational inference.
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference
Understanding the gradient variance of black-box variational inference (BBVI) is a crucial step for establishing its convergence and developing algorithmic improvements.
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients
Minimizing the inclusive Kullback-Leibler (KL) divergence with stochastic gradient descent (SGD) is challenging since its gradient is defined as an integral over the posterior.
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