Learning hyper-parameter scaling models for unsupervised anomaly detection

One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause:

• sorting respective empirical validation scores of a plurality of validated training scenarios of an anomaly detector, wherein:
• each validated training scenario has a respective training dataset to train a respective instance of the anomaly detector that is respectively configured with a respective plurality of values for a plurality of hyperparameters of the anomaly detector, and
• said respective training dataset has a respective set of values for a set of metafeatures;
• for each ranking percentage of a plurality of predefined distinct ranking percentages:
• a) selecting a subset of best training scenarios that consists of the ranking percentage of the plurality of validated training scenarios of the anomaly detector having the highest empirical validation scores;
• b) training a respective linear optimizer that infers a respective value for a particular hyperparameter of the plurality of hyperparameters of the anomaly detector, wherein the training is based on the sets of values of the set of metafeatures of the training datasets of the subset of best training scenarios and the empirical validation scores of the subset of best training scenarios,
• c) for each set of metafeatures values in a plurality of unvalidated sets of values of the set of metafeatures, wherein the set of metafeatures values does not correspond to an actual dataset:
• i) inferring, by the linear optimizer, a particular value for the particular hyperparameter of the plurality of hyperparameters of the anomaly detector;
• ii) generating, into a plurality of distinct unvalidated training scenarios, a distinct unvalidated training scenario of the anomaly detector that has:
• the set of metafeatures values and a plurality of values for the plurality of hyperparameters of the anomaly detector that contains the particular value for the particular hyperparameter;
• inferring, for each unvalidated training scenario of the plurality of distinct unvalidated training scenarios of the anomaly detector, a respective inferred validation score;
• selecting a best linear optimizer of a ranking percentage of the plurality of predefined distinct ranking percentages having a highest combined inferred validation score based on said inferred validation scores for the plurality of distinct unvalidated training scenarios of the anomaly detector;
• inferring, by the best linear optimizer of the ranking percentage of the plurality of predefined distinct ranking percentages, for a new dataset, an inferred value of the particular hyperparameter of the plurality of hyperparameters of the anomaly detector;
• and
• detecting, by a new instance of the anomaly detector that is based on the inferred value of the particular hyperparameter of the plurality of hyperparameters of the anomaly detector, an anomaly.