In variational inference, the benefits of Bayesian models rely on accurately capturing the true posterior distribution. We propose using neural samplers that specify implicit distributions, which are well-suited for approximating complex multimodal and correlated posteriors in high-dimensional spaces.

Regularization is one of the most fundamental topics in optimization, statistics and machine learning.

Until now, no tabular method has consistently outperformed classical supervised learning, which ignores these shifts.

Humans have a powerful and mysterious capacity to reason.

Monte Carlo stopping rules in a manner that is both sample efficient and robust to estimation error.