To solve this problem, convergent algorithms are developed with both single-loop and stochastic variants. Notably, this is the firstsingle-loop primalalgorithmforconstrained optimization toourknowledge.

Our results motivate a natural partial order over covariate shifts that provides a sufficient condition for determining when the shift will harm (or even help) test performance.

Inthisconte the (asymptotic) derived [68].

Then, we propose a series of DP-SignRP algorithms that leverage the robustness of the "sign flipping probability" of random projections.

At inference time, these models are computationally intensive, especially with volumetric data.