5.7k citations · 15.5k across the 75 of their papers we have counts for
11 papers · 2 filters
Sandwiching the marginal likelihood using bidirectional Monte Carlo
Roger B. Grosse, Zoubin Ghahramani, Ryan P. Adams
Computing the marginal likelihood (ML) of a model requires marginalizing out all of the parameters and latent variables, a difficult high-dimensional summation or integration probl…
A General Framework for Constrained Bayesian Optimization using Information-based Search
José Miguel Hernández-Lobato, Michael A. Gelbart, Ryan P. Adams +2
We present an information-theoretic framework for solving global black-box optimization problems that also have black-box constraints. Of particular interest to us is to efficientl…
Predictive Entropy Search for Multi-objective Bayesian Optimization
Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Amar Shah +1
We present PESMO, a Bayesian method for identifying the Pareto set of multi-objective optimization problems, when the functions are expensive to evaluate. The central idea of PESMO…
Scalable Bayesian Inference for Excitatory Point Process Networks
Scott W. Linderman, Ryan P. Adams
Networks capture our intuition about relationships in the world. They describe the friendships between Facebook users, interactions in financial markets, and synapses connecting ne…
Dependent Multinomial Models Made Easy: Stick Breaking with the Pólya-Gamma Augmentation
Scott W. Linderman, Matthew J. Johnson, Ryan P. Adams
Many practical modeling problems involve discrete data that are best represented as draws from multinomial or categorical distributions. For example, nucleotides in a DNA sequence,…
Spectral Representations for Convolutional Neural Networks
Oren Rippel, Jasper Snoek, Ryan P. Adams
Discrete Fourier transforms provide a significant speedup in the computation of convolutions in deep learning. In this work, we demonstrate that, beyond its advantages for efficien…