183 citations · 387 across the 16 of their papers we have counts for
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stat.ML2015★ 183 cited
Training generative neural networks via Maximum Mean Discrepancy optimization
Gintare Karolina Dziugaite, Daniel M. Roy, Zoubin Ghahramani
We consider training a deep neural network to generate samples from an unknown distribution given i.i.d. data. We frame learning as an optimization minimizing a two-sample test sta…
stat.ML2015★ 7 cited
Particle Gibbs for Bayesian Additive Regression Trees
Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
Additive regression trees are flexible non-parametric models and popular off-the-shelf tools for real-world non-linear regression. In application domains, such as bioinformatics, w…