In the differentially private non-convex setting, we provide several new algorithms for approximating stationary points of the population risk.

Additionally, it is evident that the results in (ii) do not implytheconvergenceof f(xk) to0.

Stochastic neural networks (SNNs) are random functions whose predictions are gained by averaging over multiple realizations.

Tostudythishypothesis, weweaponize quantizationaware training and propose a new training framework to implement adversarial quantization outcomes.

In this setting, we implementφ, by selecting the context points, and prepend the context mask: φ = [Mc,Zc]>. C.2 Pseudo-CodefortheConvNP The ConvNP can be implemented very simply by passing samples from the ConvCNP through an additional CNN decoder, which we denotedθ.

Prediction in such models can be viewed as atranslation equivariant map from observed data sets to predictiveSPs, emphasizing the intimate relationship between stationarity andequivariance.

These regret bounds nearly match lower bounds.

Therefore,wepropose three different approaches to obtain approximate solutions, as introduced in Sec. 3.