161 citations · 1k across the 69 of their papers we have counts for
9 papers · 1 filter
Scalable Unbalanced Optimal Transport using Generative Adversarial Networks
Karren D. Yang, Caroline Uhler
Generative adversarial networks (GANs) are an expressive class of neural generative models with tremendous success in modeling high-dimensional continuous measures. In this paper,…
Memorization in Overparameterized Autoencoders
Adityanarayanan Radhakrishnan, Karren Yang, Mikhail Belkin +1
The ability of deep neural networks to generalize well in the overparameterized regime has become a subject of significant research interest. We show that overparameterized autoenc…
Maximum Likelihood Estimation for Totally Positive Log-Concave Densities
Elina Robeva, Bernd Sturmfels, Ngoc Tran +1
We study nonparametric maximum likelihood estimation for two classes of multivariate distributions that imply strong forms of positive dependence; namely log-supermodular (MTP)…
Loading Monotonicity of Weighted Premiums, and Total Positivity Properties of Weight Functions
Donald Richards, Caroline Uhler
We consider the construction of insurance premiums that are monotonically increasing with respect to a loading parameter. By introducing weight functions that are totally positive…
High-Dimensional Joint Estimation of Multiple Directed Gaussian Graphical Models
Yuhao Wang, Santiago Segarra, Caroline Uhler
We consider the problem of jointly estimating multiple related directed acyclic graph (DAG) models based on high-dimensional data from each graph. This problem is motivated by the…
Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models
Raj Agrawal, Tamara Broderick, Caroline Uhler
Learning a Bayesian network (BN) from data can be useful for decision-making or discovering causal relationships. However, traditional methods often fail in modern applications, wh…
Geometry of Discrete Copulas
Elisa Perrone, Liam Solus, Caroline Uhler
Multivariate distributions are fundamental to modeling. Discrete copulas can be used to construct diverse multivariate joint distributions over random variables from estimated univ…
Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions
Karren D. Yang, Abigail Katcoff, Caroline Uhler
We consider the problem of learning causal DAGs in the setting where both observational and interventional data is available. This setting is common in biology, where gene regulato…
Direct Estimation of Differences in Causal Graphs
Yuhao Wang, Chandler Squires, Anastasiya Belyaeva +1
We consider the problem of estimating the differences between two causal directed acyclic graph (DAG) models with a shared topological order given i.i.d. samples from each model. T…