Publications (20)
Embedding by Elicitation: Dynamic Representations for Bayesian Optimization of System Prompts
Zhiyuan Jerry Lin, Benjamin Letham, Samuel Dooley +2
System prompts are a central control mechanism in modern AI systems, shaping behavior across conversations, tasks, and user populations. Yet they are difficult to tune when feedbac…
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes
Syrine Belakaria, Benjamin Letham, Janardhan Rao Doppa +3
We consider the problem of active learning for global sensitivity analysis of expensive black-box functions. Our aim is to efficiently learn the importance of different input varia…
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
Benjamin Letham, Cynthia Rudin, Tyler H. McCormick +1
We aim to produce predictive models that are not only accurate, but are also interpretable to human experts. Our models are decision lists, which consist of a series of if...then..…
Perceptual Requirements for World-Locked Rendering in AR and VR
Phillip Guan, Eric Penner, Joel Hegland +2
Stereoscopic, head-tracked display systems can show users realistic, world-locked virtual objects and environments. However, discrepancies between the rendering pipeline and physic…
Bayesian Optimization for Policy Search via Online-Offline Experimentation
Benjamin Letham, Eytan Bakshy
Online field experiments are the gold-standard way of evaluating changes to real-world interactive machine learning systems. Yet our ability to explore complex, multi-dimensional p…
Bayesian Inference of Arrival Rate and Substitution Behavior from Sales Transaction Data with Stockouts
Benjamin Letham, Lydia M. Letham, Cynthia Rudin
When an item goes out of stock, sales transaction data no longer reflect the original customer demand, since some customers leave with no purchase while others substitute alternati…