Associate Professor, University of British Columbia
2 papers at NeurIPS 2025
A general recipe for constructing tuning-free, asymptotically exact variational flows from general involutive MCMC kernels.
We prove that black-box variational inference with the mean-field Gaussian variational family converges in a rate with an explicit dimension dependence of only $O(\log d)$.
