13 citations · 22 across the 12 of their papers we have counts for
10 papers · 1 filter
Purely Agent-Driven Black-Box Optimization for Biological Design
Natalie Maus, Yimeng Zeng, Haydn Thomas Jones +11
Many key challenges in biological design -- such as small-molecule drug discovery, antimicrobial peptide development, and protein engineering -- can be framed as black-box optimiza…
We Still Don't Understand High-Dimensional Bayesian Optimization
Colin Doumont, Donney Fan, Natalie Maus +3
Existing high-dimensional Bayesian optimization (BO) methods aim to overcome the curse of dimensionality by carefully encoding structural assumptions, from locality to sparsity to…
A Dataset for Distilling Knowledge Priors from Literature for Therapeutic Design
Haydn Thomas Jones, Natalie Maus, Josh Magnus Ludan +9
AI-driven discovery can greatly reduce design time and enhance new therapeutics' effectiveness. Models using simulators explore broad design spaces but risk violating implicit cons…
Large Scale Multi-Task Bayesian Optimization with Large Language Models
Yimeng Zeng, Natalie Maus, Haydn Thomas Jones +7
In multi-task Bayesian optimization, the goal is to leverage experience from optimizing existing tasks to improve the efficiency of optimizing new ones. While approaches using mult…
Covering Multiple Objectives with a Small Set of Solutions Using Bayesian Optimization
Natalie Maus, Kyurae Kim, Yimeng Zeng +5
In multi-objective black-box optimization, the goal is typically to find solutions that optimize a set of black-box objective functions, , simultaneously. Trad…
Approximation-Aware Bayesian Optimization
Natalie Maus, Kyurae Kim, Geoff Pleiss +3
High-dimensional Bayesian optimization (BO) tasks such as molecular design often require 10,000 function evaluations before obtaining meaningful results. While methods like sparse…