106 citations · 139 across the 5 of their papers we have counts for
6 papers
Learning Certifiably Optimal Rule Lists for Categorical Data
Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi +2
We present the design and implementation of a custom discrete optimization technique for building rule lists over a categorical feature space. Our algorithm produces rule lists wit…
Patterns of Scalable Bayesian Inference
Elaine Angelino, Matthew James Johnson, Ryan P. Adams
Datasets are growing not just in size but in complexity, creating a demand for rich models and quantification of uncertainty. Bayesian methods are an excellent fit for this demand,…
Variational consensus Monte Carlo
Maxim Rabinovich, Elaine Angelino, Michael I. Jordan
Practitioners of Bayesian statistics have long depended on Markov chain Monte Carlo (MCMC) to obtain samples from intractable posterior distributions. Unfortunately, MCMC algorithm…
Accelerating MCMC via Parallel Predictive Prefetching
Elaine Angelino, Eddie Kohler, Amos Waterland +2
We present a general framework for accelerating a large class of widely used Markov chain Monte Carlo (MCMC) algorithms. Our approach exploits fast, iterative approximations to the…
Attribute-Efficient Evolvability of Linear Functions
Elaine Angelino, Varun Kanade
In a seminal paper, Valiant (2006) introduced a computational model for evolution to address the question of complexity that can arise through Darwinian mechanisms. Valiant views e…
External-Memory Multimaps
Elaine Angelino, Michael T. Goodrich, Michael Mitzenmacher +1
Many data structures support dictionaries, also known as maps or associative arrays, which store and manage a set of key-value pairs. A \emph{multimap} is generalization that allow…