1 citations · 1 across the 7 of their papers we have counts for
7 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…
Knowledge Gradient for Preference Learning
Kaiwen Wu, Jacob R. Gardner
The knowledge gradient is a popular acquisition function in Bayesian optimization (BO) for optimizing black-box objectives with noisy function evaluations. Many practical settings,…
Mixed Likelihood Variational Gaussian Processes
Kaiwen Wu, Craig Sanders, Benjamin Letham +1
Gaussian processes (GPs) are powerful models for human-in-the-loop experiments due to their flexibility and well-calibrated uncertainty. However, GPs modeling human responses typic…
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference
Jonathan Wenger, Kaiwen Wu, Philipp Hennig +3
Model selection in Gaussian processes scales prohibitively with the size of the training dataset, both in time and memory. While many approximations exist, all incur inevitable app…
A Fast, Robust Elliptical Slice Sampling Implementation for Linearly Truncated Multivariate Normal Distributions
Kaiwen Wu, Jacob R. Gardner
Elliptical slice sampling, when adapted to linearly truncated multivariate normal distributions, is a rejection-free Markov chain Monte Carlo method. At its core, it requires analy…
Understanding Stochastic Natural Gradient Variational Inference
Kaiwen Wu, Jacob R. Gardner
Stochastic natural gradient variational inference (NGVI) is a popular posterior inference method with applications in various probabilistic models. Despite its wide usage, little i…
Large-Scale Gaussian Processes via Alternating Projection
Kaiwen Wu, Jonathan Wenger, Haydn Jones +2
Training and inference in Gaussian processes (GPs) require solving linear systems with kernel matrices. To address the prohibitive time complexity, r…