7 citations · 14 across the 9 of their papers we have counts for
4 papers · 1 filter
Multiple Domain Causal Networks
Tianhui Zhou, William E. Carson, Michael Hunter Klein +1
Observational studies are regarded as economic alternatives to randomized trials, often used in their stead to investigate and determine treatment efficacy. Due to lack of sample s…
AugmentedPCA: A Python Package of Supervised and Adversarial Linear Factor Models
William E. Carson, Austin Talbot, David Carlson
Deep autoencoders are often extended with a supervised or adversarial loss to learn latent representations with desirable properties, such as greater predictivity of labels and out…
On Target Shift in Adversarial Domain Adaptation
Yitong Li, Michael Murias, Samantha Major +2
Discrepancy between training and testing domains is a fundamental problem in the generalization of machine learning techniques. Recently, several approaches have been proposed to l…
Partition Functions from Rao-Blackwellized Tempered Sampling
David Carlson, Patrick Stinson, Ari Pakman +1
Partition functions of probability distributions are important quantities for model evaluation and comparisons. We present a new method to compute partition functions of complex an…