33 citations · 74 across the 14 of their papers we have counts for
17 papers
SoftServe: A Scalable Quasi-Newton Method for Deep Learning
Joohwan Ko, Tetiana Parshakova, Diana Cai +1
Quasi-Newton (QN) methods have long been among the most effective methods for large-scale unconstrained convex optimization. Two obstacles have limited their use in deep learning:…
Simulation-Based Empirical Bayes
Xinwei Shen, Diana Cai, Cheng Zhang +1
Empirical Bayes (EB) performs simultaneous inference across many related latent variables. Classical EB assumes that the likelihood p(x | z) is tractable. In many scientific applic…
Fisher meets Feynman: score-based variational inference with a product of experts
Diana Cai, Robert M. Gower, David M. Blei +1
We introduce a highly expressive yet distinctly tractable family for black-box variational inference (BBVI). Each member of this family is a weighted product of experts (PoE), and…
EigenVI: score-based variational inference with orthogonal function expansions
Diana Cai, Chirag Modi, Charles C. Margossian +3
We develop EigenVI, an eigenvalue-based approach for black-box variational inference (BBVI). EigenVI constructs its variational approximations from orthogonal function expansions.…
Batch, match, and patch: low-rank approximations for score-based variational inference
Chirag Modi, Diana Cai, Lawrence K. Saul
Black-box variational inference (BBVI) scales poorly to high-dimensional problems when it is used to estimate a multivariate Gaussian approximation with a full covariance matrix. I…
Probabilistic Prediction of Material Stability: Integrating Convex Hulls into Active Learning
Andrew Novick, Diana Cai, Quan Nguyen +3
Active learning is a valuable tool for efficiently exploring complex spaces, finding a variety of uses in materials science. However, the determination of convex hulls for phase di…
Batch and match: black-box variational inference with a score-based divergence
Diana Cai, Chirag Modi, Loucas Pillaud-Vivien +4
Most leading implementations of black-box variational inference (BBVI) are based on optimizing a stochastic evidence lower bound (ELBO). But such approaches to BBVI often converge…
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…
Finite mixture models do not reliably learn the number of components
Diana Cai, Trevor Campbell, Tamara Broderick
Scientists and engineers are often interested in learning the number of subpopulations (or components) present in a data set. A common suggestion is to use a finite mixture model (…
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…
Edge-exchangeable graphs and sparsity (NIPS 2016)
Diana Cai, Trevor Campbell, Tamara Broderick
Many popular network models rely on the assumption of (vertex) exchangeability, in which the distribution of the graph is invariant to relabelings of the vertices. However, the Ald…
Exchangeable Trait Allocations
Trevor Campbell, Diana Cai, Tamara Broderick
Trait allocations are a class of combinatorial structures in which data may belong to multiple groups and may have different levels of belonging in each group. Often the data are a…
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…
Priors on exchangeable directed graphs
Diana Cai, Nathanael Ackerman, Cameron Freer
Directed graphs occur throughout statistical modeling of networks, and exchangeability is a natural assumption when the ordering of vertices does not matter. There is a deep struct…
An iterative step-function estimator for graphons
Diana Cai, Nathanael Ackerman, Cameron Freer
Exchangeable graphs arise via a sampling procedure from measurable functions known as graphons. A natural estimation problem is how well we can recover a graphon given a single gra…