Publications

Research papers grouped by primary theme. For a complete citation index, see Google Scholar.

Human Feedback, Preference Learning & Adaptive AI

Bayesian optimization, adaptive experimentation, and decision-theoretic methods for turning human feedback, preferences, natural-language signals, and uncertainty into evaluation and optimization signals for AI systems and complex decisions.

Human feedback Preference learning Bayesian optimization Adaptive experimentation Decision-making under uncertainty Model evaluation

Embedding by Elicitation: Dynamic Representations for Bayesian Optimization of System Prompts

Zhiyuan Jerry Lin,
Benjamin Letham,
Samuel Dooley,
Maximilian Balandat,
Eytan Bakshy

Preprint 2026.

LILO: Bayesian Optimization with Natural Language Feedback

Katarzyna Kobalczyk,
Zhiyuan Jerry Lin,
Benjamin Letham,
Zhuokai Zhao,
Maximilian Balandat,
Eytan Bakshy

Conference, ICML 2026.

Ax: A Platform for Adaptive Experimentation

Miles Olson,
Elizabeth Santorella,
Louis C. Tiao,
Sait Cakmak,
Mia Garrard,
Samuel Daulton,
Zhiyuan Jerry Lin,
Sebastian Ament,
Bernard Beckerman,
Eric Onofrey,
Paschal Igusti,
Cristian Lara,
Benjamin Letham,
Cesar Cardoso,
Shiyun Sunny Shen,
Andy Chenyuan Lin,
Matthew Grange,
Elena Kashtelyan,
David Eriksson,
Maximilian Balandat,
Eytan Bakshy

Conference, AutoML 2025.

Bayesian Optimization of High-dimensional Outputs with Human Feedback

Qing Feng,
Zhiyuan Jerry Lin,
Yujia Zhang,
Benjamin Letham,
Jelena Markovic-Voronov,
Ryan-Rhys Griffiths,
Peter I. Frazier,
Eytan Bakshy

Workshop, NeurIPS Bayesian Decision-making and Uncertainty 2024.

Robust Predictions with Ambiguous Time Delays: A Bootstrap Strategy

Jiajie Wang,
Zhiyuan Jerry Lin,
Wen Chen

Conference, KDD 2024.

Joint Composite Latent Space Bayesian Optimization

Natalie Maus,
Zhiyuan Jerry Lin,
Maximilian Balandat,
Eytan Bakshy

Conference, ICML 2024.

qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization

Raul Astudillo,
Zhiyuan Jerry Lin,
Eytan Bakshy,
Peter I. Frazier

Conference, AISTATS 2023.

Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes

Zhiyuan Jerry Lin,
Raul Astudillo,
Peter I. Frazier,
Eytan Bakshy

Conference, AISTATS 2022.

Probability Paths and the Structure of Predictions over Time

Zhiyuan Jerry Lin,
Hao Sheng,
Sharad Goel

Conference, NeurIPS 2021.

Bandit Algorithms to Personalize Educational Chatbots

William Cai,
Joshua Grossman,
Zhiyuan (Jerry) Lin,
Hao Sheng,
Johnny Tian-Zheng Wei,
Joseph Jay Williams,
Sharad Goel

Journal, Machine Learning, 2021.

Interactive Preference Learning For Multi-Objective Bayesian Optimization in Online Experiments

Zhiyuan Lin,
Eytan Bakshy

Work-in-progress Extended Abstract, CODE 2020.

Preference Learning for Real-World Multi-Objective Decision Making

Zhiyuan (Jerry) Lin,
Adam Obeng,
Eytan Bakshy

Workshop, RealML @ ICML 2020.

Preference Learning for Multi-Objective Decision Making in Online Experiments

Zhiyuan Lin,
Adam Obeng,
Eytan Bakshy

Work-in-progress Extended Abstract, CODE 2019.

Graphs, Visualization & Scalable Computation

Methods and systems for exploring, visualizing, and computing over large graph-structured data.

Graphs Visualization Visual analytics Graph mining Scalable computation

FACETS: Adaptive Local Exploration of Large Graphs

Robert Pienta,
Minsuk Kahng,
Zhiyuan Lin,
Jilles Vreeken,
Partha Talukdar,
James Abello,
Ganesh Parameswaran,
Duen Horng Chau

Conference, SIAM SDM 2017.

AdaptiveNav: Adaptive Discovery of Interesting and Surprising Nodes in Large Graphs

Robert Pienta,
Zhiyuan Lin,
Minsuk Kahng,
Jilles Vreeken,
Partha P. Talukdar,
James Abello,
Ganesh Parameswaran,
Duen Horng (Polo) Chau

Poster Abstract, IEEE VIS 2015.

GLO-STIX: Graph-Level Operations for Specifying Techniques and Interactive eXploration

Charles D. Stolper,
Minsuk Kahng,
Zhiyuan Lin,
Florian Foerster,
Aakash Goel,
John Stasko,
Duen Horng Chau

Conference, IEEE InfoVis 2014.

Journal, IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 12, pp. 2320–2328.

Towards Scalable Graph Computation on Mobile Devices

Yiqi Chen,
Zhiyuan Lin,
Robert Pienta,
Minsuk Kahng,
Duen Horng Chau

Workshop, IEEE BigData'14 Scalable Machine Learning: Theory and Applications.

MMap: Fast Billion-Scale Graph Computation on a PC via Memory Mapping

Zhiyuan Lin,
Minsuk Kahng,
Kaeser Md. Sabrin,
Duen Horng Chau,
Ho Lee,
U Kang

Conference, IEEE BigData 2014.

GLOs: Graph-Level Operations for Exploratory Network Visualization

Charles D. Stolper,
Florian Foerster,
Minsuk Kahng,
Zhiyuan Lin,
Aakash Goel,
John Stasko,
Duen Horng Chau

Work-In-Progress Poster, CHI 2014.

Demonstrating Interactive Multi-resolution Large Graph Exploration

Zhiyuan Lin,
Nan Cao,
Hanghang Tong,
Fei Wang,
U Kang,
Duen Horng Chau

Demo, ICDM 2013.

Interactive Multi-resolution Exploration of Million Node Graphs

Zhiyuan Lin,
Nan Cao,
Hanghang Tong,
Fei Wang,
U Kang,
Duen Horng Chau

Poster Abstract, IEEE VIS 2013.

Leveraging Memory Mapping for Fast and Scalable Graph Computation on a PC

Zhiyuan Lin,
Duen Horng Chau,
U Kang

Workshop, IEEE BigData'13 Scalable Machine Learning: Theory and Applications.

Scalable, Minimalist Graph Computation on a PC via Memory Mapping

Zhiyuan Lin,
Duen Horng Chau,
U Kang

Workshop, Workshop on Information in Networks (WIN 2013).

Non-Academic Publications

In the U.S. criminal justice system, algorithms help officials make better decisions, our research finds

Zhiyuan (Jerry) Lin,
Jongbin Jung,
Sharad Goel,
Jennifer Skeem

Washington Post.

The Art of Machine-Human Translation

Jerry Lin

The Tower (research journal for undergraduates at Georgia Tech), Volume VII, Issue I, Page 20.

Interactive Preference Learning For Multi-Objective Bayesian Optimization in Online Experiments

Preference Learning for Multi-Objective Decision Making in Online Experiments

Quantifying and Predicting Mental Illness Severity in Online Pro-Eating Disorder Communities Honorable Mention

Scalable, Minimalist Graph Computation on a PC via Memory Mapping

In the U.S. criminal justice system, algorithms help officials make better decisions, our research finds

The Art of Machine-Human Translation