528 citations · 1.1k across the 28 of their papers we have counts for
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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…
Learning to Select Pivotal Samples for Meta Re-weighting
Yinjun Wu, Adam Stein, Jacob Gardner +1
Sample re-weighting strategies provide a promising mechanism to deal with imperfect training data in machine learning, such as noisily labeled or class-imbalanced data. One such st…