Machine learning (ML) models often exhibit unexpected failures once deployed in the "wild".

We do not foresee direct negative societal impact from the current work. Also, one may argue that our work is catalytic to enhancing the'disclosure risk' of individuals, i.e. an adversary might be able to make accurate Granted, no existing privacy frameworks can guard against this. We prove its ergodicity in Theorem S-3.1, which implies Theorem 3.3 . The model is such that the set { x: f ( x |) > 0 } does not depend on . The Metropolis-within-Gibbs sampler is aperiodic by construction, since some proposals can be rejected.

Currently, the data augmentation parameters are chosen by human effort and costly cross-validation, which makes it cumbersome to apply to new datasets. We develop a convenient gradient-based method for selecting the data augmentation without validation data during training of a deep neural network.

Because the true underlying structure of the data is rarely accessible, this "representation learning" must be

For the GP, the bias-variance trade-off is made by optimization of the two hyperparameters: the length scale and noise-term.