2 citations · 2 across the 8 of their papers we have counts for
9 papers
Self-Driving Datasets: From 20 Million Papers to Nuanced Biomedical Knowledge at Scale
Haydn Jones, Yimeng Zeng, Alden Rose +11
Manually curated biomedical repositories -- spanning bioactivity, genomics, and chemistry -- are expensive to maintain, lag behind primary literature, and discard experimental cont…
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…
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…
Improving Structural Diversity of Blackbox LLMs via Chain-of-Specification Prompting
Halley Young, Yimeng Zeng, Jacob Gardner +1
The capability to generate diverse text is a key challenge facing large language models (LLMs). Thus far, diversity has been studied via metrics such as -gram diversity or diver…
Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity
Wentao Guo, Jikai Long, Yimeng Zeng +9
Zeroth-order optimization (ZO) is a memory-efficient strategy for fine-tuning Large Language Models using only forward passes. However, the application of ZO fine-tuning in memory-…
Generative Adversarial Model-Based Optimization via Source Critic Regularization
Michael S. Yao, Yimeng Zeng, Hamsa Bastani +3
Offline model-based optimization seeks to optimize against a learned surrogate model without querying the true oracle objective function during optimization. Such tasks are commonl…