15 citations · 25 across the 19 of their papers we have counts for
4 papers · 1 filter
Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?
Kyurae Kim, Yian Ma, Jacob R. Gardner
We prove that black-box variational inference (BBVI) with control variates, particularly the sticking-the-landing (STL) estimator, converges at a geometric (traditionally called "l…
The Behavior and Convergence of Local Bayesian Optimization
Kaiwen Wu, Kyurae Kim, Roman Garnett +1
A recent development in Bayesian optimization is the use of local optimization strategies, which can deliver strong empirical performance on high-dimensional problems compared to t…
On the Convergence of Black-Box Variational Inference
Kyurae Kim, Jisu Oh, Kaiwen Wu +2
We provide the first convergence guarantee for full black-box variational inference (BBVI), also known as Monte Carlo variational inference. While preliminary investigations worked…
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference
Kyurae Kim, Kaiwen Wu, Jisu Oh +1
Understanding the gradient variance of black-box variational inference (BBVI) is a crucial step for establishing its convergence and developing algorithmic improvements. However, e…