International Business Machines Corporation

Marek Petrik

Sechan Oh

Amit Dhurandhar

Markus Ettl

Soumyadip Ghosh

Stacey C Ramos

Rajesh Kumar Ravi

Adam N Elmachtoub

Techniques facilitating interpretable rule generation using loss-preserving transformation are provided. In one example, a computer-implemented method can comprise evaluating, by a system operatively coupled to a processor, an input data set that comprises three data categories. The computer-implemented method can also comprise transforming, by the system, the input data set into a transformed data set. The transformed data set can comprise two data categories determined based on the three data categories. Transforming the input data set can comprise determining a first cost associated with the transformed data set is no greater than a second cost associated with the input data set.

Interpretable rule generation using loss preserving transformation

Interpretable rule generation using loss-preserving transformation

A system, method and computer program product for determining a target population to be subject to a promotion or offer of goods/services. The target population is determined that strongly prefers a given promotion, while also making sure the target population represents a sizable number of consumers such that profits may be maximized. The system provides an output solution listing available promotion options and one or more corresponding target groups based on solving an optimization problem that incorporates, prior obtained most important customer features for each promotion using historical promotion data and statistics measures. The system may automatically initiate a promotion offering to each of said customers by communicating the promotion to the members of the targeted group of people, wherein a percentage of future transactions to which the promotion is offered is expected to exceed a threshold level.

Determining feature importance and target population in the context of promotion recommendation

Assessing resiliency of facility configuration strategies, in one aspect, may employ a mathematical model representation of facility configuration. Organization environment factors and one or more desired performance metrics associated with the organization may be determined. A technical model specification that defines the configuration of facilities of the organization may be received, and a mathematical model representation of the configuration may be created. The mathematical model representation is solved for steady-state characteristics, and one or more values may be estimated respectively of the desired performance metrics based on the steady-state characteristics. The resiliency may be measured based on the estimated one or more values.

Assessment and rationalization of resiliency of data center strategies

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Marek Petrik