Jacob R. Gardner

Scaling Multi-Task Bayesian Optimization with Large Language Models

Making the Subspace Assumption Work: Low-Dimensional Exploration for High-Dimensional Bayesian Optimization

Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference

Covering Multiple Objectives with a Small Set of Solutions Using Bayesian Optimization

Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization

Zeroth-Order Fine-Tuning of LLMs with Transferable Static Sparsity