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Found 6 papers, 3 papers with code
Learning Action Embeddings for Off-Policy Evaluation
Off-policy evaluation (OPE) methods allow us to compute the expected reward of a policy by using the logged data collected by a different policy.
Variational Boosted Soft Trees
Variational inference is often used to implement Bayesian neural networks, but is difficult to apply to GBMs, because the decision trees used as weak learners are non-differentiable.
Ordering Dimensions with Nested Dropout Normalizing Flows
Like in PCA, the leading latent dimensions define a sequence of manifolds that lie close to the data.
Neural Spline Flows
A normalizing flow models a complex probability density as an invertible transformation of a simple base density.
Cubic-Spline Flows
A normalizing flow models a complex probability density as an invertible transformation of a simple density.
Bayesian Adversarial Spheres: Bayesian Inference and Adversarial Examples in a Noiseless Setting
Modern deep neural network models suffer from adversarial examples, i. e. confidently misclassified points in the input space.
