There is a growing need for machine learning-based anomaly detection strategies to broaden the search Beyond-the-Standard-Model (BSM) physics at Large Hadron Collider (LHC) and elsewhere. The first step of any approach specify observables then use them decide on set anomalous events. One common choice select events that have low probability density. It well-known fact densities are not invarian...

We present an improved analysis of mini-batched stochastic dual coordinate ascent for regularized empirical loss minimization (i.e. SVM and SVM-type objectives). Our analysis allows for flexible sampling schemes, including where data is distribute across machines, and combines a dependence on the smoothness of the loss and/or the data spread (measured through the spectral norm).