13 citations · 34 across the 13 of their papers we have counts for
14 papers
Adversarial Query Synthesis via Bayesian Optimization
Jeffrey Tao, Yimeng Zeng, Haydn Thomas Jones +4
Benchmark workloads are extremely important to the database management research community, especially as more machine learning components are integrated into database systems. Here…
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…
Electron-affinity difference distributions as an organizing principle for superconductivity, enabling the discovery of PtPbBi
Omri Lesser, Yanjun Liu, Natalie Maus +11
Predicting the superconducting transition temperature () from crystal structure and composition remains a central challenge in condensed-matter physics, reflecting the absence…
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…
Learned Offline Query Planning via Bayesian Optimization
Jeffrey Tao, Natalie Maus, Haydn Jones +3
Analytics database workloads often contain queries that are executed repeatedly. Existing optimization techniques generally prioritize keeping optimization cost low, normally well…
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…
Joint Composite Latent Space Bayesian Optimization
Natalie Maus, Zhiyuan Jerry Lin, Maximilian Balandat +1
Bayesian Optimization (BO) is a technique for sample-efficient black-box optimization that employs probabilistic models to identify promising input locations for evaluation. When d…
Inverse Protein Folding Using Deep Bayesian Optimization
Natalie Maus, Yimeng Zeng, Daniel Allen Anderson +5
Inverse protein folding -- the task of predicting a protein sequence from its backbone atom coordinates -- has surfaced as an important problem in the "top down", de novo design of…
Black Box Adversarial Prompting for Foundation Models
Natalie Maus, Patrick Chao, Eric Wong +1
Prompting interfaces allow users to quickly adjust the output of generative models in both vision and language. However, small changes and design choices in the prompt can lead to…
Discovering Many Diverse Solutions with Bayesian Optimization
Natalie Maus, Kaiwen Wu, David Eriksson +1
Bayesian optimization (BO) is a popular approach for sample-efficient optimization of black-box objective functions. While BO has been successfully applied to a wide range of scien…
Local Latent Space Bayesian Optimization over Structured Inputs
Natalie Maus, Haydn T. Jones, Juston S. Moore +3
Bayesian optimization over the latent spaces of deep autoencoder models (DAEs) has recently emerged as a promising new approach for optimizing challenging black-box functions over…