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Adversarial Bayesian Simulation
In the absence of explicit or tractable likelihoods, Bayesians often resort to approximate Bayesian computation (ABC) for inference.
Approximate Bayesian Computation via Classification
We demonstrate the usefulness of our approach on simulated examples as well as real data in the context of stock volatility estimation.
Uncertainty Quantification for Sparse Deep Learning
Deep learning methods continue to have a decided impact on machine learning, both in theory and in practice.
Data Augmentation for Bayesian Deep Learning
Our methodology is compared to traditional stochastic gradient descent with back-propagation.
Sparse Regularization in Marketing and Economics
Alpha-norm, in contrast to lasso and ridge regularization, jumps to a sparse solution.
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