15 citations · 25 across the 19 of their papers we have counts for
5 papers · 1 filter
Bayesian Wideband Signal Detection via Source Signal Marginalization and RJMCMC
Kyurae Kim, Philip T. Clemson, James P. Reilly +2
Consider an array receiving unknown wideband signals from an unknown number of sources . Wideband signals can occupy arbitrarily wide bandwidths, rendering demodulation-based ap…
Approximation-Aware Bayesian Optimization
Natalie Maus, Kyurae Kim, Geoff Pleiss +3
High-dimensional Bayesian optimization (BO) tasks such as molecular design often require 10,000 function evaluations before obtaining meaningful results. While methods like sparse…
Demystifying SGD with Doubly Stochastic Gradients
Kyurae Kim, Joohwan Ko, Yi-An Ma +1
Optimization objectives in the form of a sum of intractable expectations are rising in importance (e.g., diffusion models, variational autoencoders, and many more), a setting also…
Stochastic Approximation with Biased MCMC for Expectation Maximization
Samuel Gruffaz, Kyurae Kim, Alain Oliviero Durmus +1
The expectation maximization (EM) algorithm is a widespread method for empirical Bayesian inference, but its expectation step (E-step) is often intractable. Employing a stochastic…
Provably Scalable Black-Box Variational Inference with Structured Variational Families
Joohwan Ko, Kyurae Kim, Woo Chang Kim +1
Variational families with full-rank covariance approximations are known not to work well in black-box variational inference (BBVI), both empirically and theoretically. In fact, rec…