421 citations · 3.9k across the 201 of their papers we have counts for
38 papers · 1 filter
Uncertain Bayesian Networks: Learning from Incomplete Data
Conrad D. Hougen, Lance M. Kaplan, Federico Cerutti +1
When the historical data are limited, the conditional probabilities associated with the nodes of Bayesian networks are uncertain and can be empirically estimated. Second order esti…
High dimensional stochastic linear contextual bandit with missing covariates
Byoungwook Jang, Julia Nepper, Marc Chevrette +2
Recent works in bandit problems adopted lasso convergence theory in the sequential decision-making setting. Even with fully observed contexts, there are technical challenges that h…
Multiway Ensemble Kalman Filter
Yu Wang, Alfred Hero
In this work, we study the emergence of sparsity and multiway structures in second-order statistical characterizations of dynamical processes governed by partial differential equat…
Fair Community Detection and Structure Learning in Heterogeneous Graphical Models
Davoud Ataee Tarzanagh, Laura Balzano, Alfred O. Hero
Inference of community structure in probabilistic graphical models may not be consistent with fairness constraints when nodes have demographic attributes. Certain demographics may…
SG-PALM: a Fast Physically Interpretable Tensor Graphical Model
Yu Wang, Alfred Hero
We propose a new graphical model inference procedure, called SG-PALM, for learning conditional dependency structure of high-dimensional tensor-variate data. Unlike most other tenso…
The Power of Graph Convolutional Networks to Distinguish Random Graph Models: Short Version
Abram Magner, Mayank Baranwal, Alfred O. Hero
Graph convolutional networks (GCNs) are a widely used method for graph representation learning. We investigate the power of GCNs, as a function of their number of layers, to distin…