Our results concisely explain the interplay between the learning rate, the noise variance in the gradient oracle, and the strength ofthetime drift. The high-probability results merely assume that thegradient noise and time drift have light tails. Moreover, none of the results require the objectives to have bounded domains.

Our results concisely explain the interplay between the learning rate, the noise variance in the gradient oracle, and the strength ofthetime drift. The high-probability results merely assume that thegradient noise and time drift have light tails. Moreover, none of the results require the objectives to have bounded domains.

Despite the extensive application of multi-pass SGD in practice, there are only a few theoretical techniques being developed to study the generalization of multi-pass SGD.

Despite the extensive application of multi-pass SGD in practice, there are only a few theoretical techniques being developed to study the generalization of multi-pass SGD.

Such slot-based approaches have several conceptual limitations.

Anomaly detection is widely used for identifying critical errors and suspicious behaviors, butcurrent methods lackinterpretability. Weleverage common propertiesofexisting methods andrecent advancesingenerativemodels tointroduce counterfactual explanations for anomaly detection. Givenan input, we generate its counterfactual as a diffusion-based repair that shows what a non-anomalous versionshouldhavelookedlike.