5 papers
Multi-fidelity Monte Carlo: a pseudo-marginal approach
Diana Cai, Ryan P. Adams
Markov chain Monte Carlo (MCMC) is an established approach for uncertainty quantification and propagation in scientific applications. A key challenge in applying MCMC to scientific…
Active multi-fidelity Bayesian online changepoint detection
Gregory W. Gundersen, Diana Cai, Chuteng Zhou +2
Online algorithms for detecting changepoints, or abrupt shifts in the behavior of a time series, are often deployed with limited resources, e.g., to edge computing settings such as…
Weighted Meta-Learning
Diana Cai, Rishit Sheth, Lester Mackey +1
Meta-learning leverages related source tasks to learn an initialization that can be quickly fine-tuned to a target task with limited labeled examples. However, many popular meta-le…
Completely random measures for modeling power laws in sparse graphs
Diana Cai, Tamara Broderick
Network data appear in a number of applications, such as online social networks and biological networks, and there is growing interest in both developing models for networks as wel…
Edge-exchangeable graphs and sparsity
Tamara Broderick, Diana Cai
A known failing of many popular random graph models is that the Aldous-Hoover Theorem guarantees these graphs are dense with probability one; that is, the number of edges grows qua…