research
∙
06/08/2023
Representing and Learning Functions Invariant Under Crystallographic Groups
Crystallographic groups describe the symmetries of crystals and other re...
share
research
∙
02/18/2022
Quantifying the Effects of Data Augmentation
We provide results that exactly quantify how data augmentation affects t...
research
∙
06/27/2018
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data
Empirical risk minimization is the principal tool for prediction problem...
research
∙
06/27/2018
Limit theorems for invariant distributions
We consider random processes whose distribution satisfies a symmetry pro...
research
∙
04/16/2018
Compressibility and Generalization in Large-Scale Deep Learning
Modern neural networks are highly overparameterized, with capacity to su...
research
∙
10/11/2017
Subsampling large graphs and invariance in networks
Specify a randomized algorithm that, given a very large graph or network...
research
∙
12/19/2016
Random Walk Models of Network Formation and Sequential Monte Carlo Methods for Graphs
We introduce a class of network models that insert edges by connecting t...
research
∙
12/30/2013
Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structures
The natural habitat of most Bayesian methods is data represented by exch...
research
∙
01/24/2011
Projective Limit Random Probabilities on Polish Spaces
A pivotal problem in Bayesian nonparametrics is the construction of prio...
research
∙
12/02/2010
