We consider linear prediction with a convex Lipschitz loss, or more generally, stochastic convex optimization problems of generalized linear form, i.e.

Much of the work in online learning focuses on the study of sublinear upper bounds on the regret.

We adopt four bioinformatics datasets in the experiment. Given the input graph, it will randomly add or cut a certain portion ofconnections between nodes withtheprobability of0.2. It will set the feature of 20% nodes in the graph to Gaussian noises with mean and standard deviation is 0.5. We adopt the Adam [5] optimizer, which is a variant of Stochastic Gradient Descent (SGD) with adaptivemoment estimation.

Our datasets and code are available via the following links: Github: https://github.com/NREL/BuildingsBench As described in Sec. 3 and Sec. 4, Buildings-900K and the BuildingsBench benchmark datasets are B.1 Motivation Q: For what purpose was the dataset created? It specifically addresses a lack of appropriately sized and diverse datasets for pretraining STLF models. We emphasize that the EULP was not originally developed for studying STLF. Rather, it was developed as a general resource to "...help electric utilities, grid operators, manufacturers, Q: Who created the dataset (e.g., which team, research group) and on behalf of which entity Q: Who funded the creation of the dataset?