Jpmorgan Chase Bank, N.a.

Michael Cashmore

Daniele Magazzeni

Manuela Veloso

Daniel Borrajo

Parisa Zehtabi

Alberto Pozanco

Jie Deng

Yufei Wu

Danial Dervovic

A method for evaluating classification models that are trained by using incomplete data sets for which data is known to be missing and the missing data is known to be non-random is provided. The method includes: receiving first information that relates to data to be used for training and evaluating a performance of a classification model that is designed to make a determination with respect to a particular query; analyzing the first information to determine second information that relates to a known portion of the first information and third information that relates to missing data; estimating, based on the third information, an uncertainty that corresponds to the missing data; and calculating, based on the second information and the estimated uncertainty, a first Gaussian approximation to a performance metric that relates to the first classification model.

Method and system for evaluation of classification models

Methods and systems for performing a combinatorial optimization task are provided. The method includes: receiving a first set of data items and a second set of data items; dividing the second set of data items into a first subset and a second subset; generating, based on the first subset, a first set of subset sums, and storing the first set of subset sums as a list; generating, based on the second subset, a second set of subset sums, and storing the second set of subset sums as a hash-map in conjunction with a corresponding hash function; and detecting, based on the first set of data items, the list, and the hash-map, at least one match between a subset sum that corresponds to the second set of data items and a subset sum that corresponds to the first set of data items.

Method and system for solving subset sum matching problem using search approach

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Stirlingshire, United Kingdom

Michael Cashmore