113 citations · 294 across the 25 of their papers we have counts for
3 papers · 1 filter
Challenges and Opportunities in High-dimensional Variational Inference
Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe +3
Current black-box variational inference (BBVI) methods require the user to make numerous design choices -- such as the selection of variational objective and approximating family -…
Robust, Accurate Stochastic Optimization for Variational Inference
Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen +3
We consider the problem of fitting variational posterior approximations using stochastic optimization methods. The performance of these approximations depends on (1) how well the v…
Risk and Regret of Hierarchical Bayesian Learners
Jonathan H. Huggins, Joshua B. Tenenbaum
Common statistical practice has shown that the full power of Bayesian methods is not realized until hierarchical priors are used, as these allow for greater "robustness" and the ab…