Entropic Gromov-Wasserstein Distances: Stability and Algorithms

Bayesian Meta-Learning Is All You Need — James Le

This blog post is my attempt to demystify the probabilistic view of meta-learning and answer these key questions: Why is the deterministic view of meta-learning not sufficient? What is the variational inference? How can we design neural-based Bayesian meta-learning algorithms?

Structured Optimal Variational Inference for Dynamic Latent Space Models

A Framework for Improving the Reliability of Black-box Variational Inference

Approximation-Aware Bayesian Optimization

High-dimensional Bayesian optimization (BO) tasks such as molecular design often require 10,000 function evaluations before obtaining meaningful results. While methods like sparse variational Gaussian processes (SVGPs) reduce computational requirements in these settings, the underlying approximations result in suboptimal data acquisitions that slow the progress of optimization. In this paper we modify SVGPs to better align with the goals of BO: targeting informed data acquisition rather than global posterior fidelity. Using the framework of utility-calibrated variational inference, we unify GP approximation and data acquisition into a joint optimization problem, thereby ensuring optimal decisions under a limited computational budget. Our approach can be used with any decision-theoretic acquisition function and is compatible with trust region methods like TuRBO. We derive efficient joint objectives for the expected improvement and knowledge gradient acquisition functions in both the standard and batch BO settings. Our approach outperforms standard SVGPs on high-dimensional benchmark tasks in control and molecular design.

A Variational Approach to Bayesian Phylogenetic Inference

Low-rank Variational Bayes correction to the Laplace method

Additive smoothing error in backward variational inference for general state-space models

Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box