5.7k citations · 15.5k across the 75 of their papers we have counts for
12 papers · 2 filters
Graph-Sparse LDA: A Topic Model with Structured Sparsity
Finale Doshi-Velez, Byron Wallace, Ryan Adams
Originally designed to model text, topic modeling has become a powerful tool for uncovering latent structure in domains including medicine, finance, and vision. The goals for the m…
A framework for studying synaptic plasticity with neural spike train data
Scott W. Linderman, Christopher H. Stock, Ryan P. Adams
Learning and memory in the brain are implemented by complex, time-varying changes in neural circuitry. The computational rules according to which synaptic weights change over time…
Freeze-Thaw Bayesian Optimization
Kevin Swersky, Jasper Snoek, Ryan Prescott Adams
In this paper we develop a dynamic form of Bayesian optimization for machine learning models with the goal of rapidly finding good hyperparameter settings. Our method uses the part…
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…
Firefly Monte Carlo: Exact MCMC with Subsets of Data
Dougal Maclaurin, Ryan P. Adams
Markov chain Monte Carlo (MCMC) is a popular and successful general-purpose tool for Bayesian inference. However, MCMC cannot be practically applied to large data sets because of t…
Bayesian Optimization with Unknown Constraints
Michael A. Gelbart, Jasper Snoek, Ryan P. Adams
Recent work on Bayesian optimization has shown its effectiveness in global optimization of difficult black-box objective functions. Many real-world optimization problems of interes…