Capital One Services, LLC

Jeremy Gerstle

Santosh Bardwaj

Tao Tao

Yihui Tang

Il Sun Yoo

Daniel Jumper

Jonathan Boroumand

Michael Walters

Erik Virbitsky

Jianshi Zhao

Olufemi Anthony

Aniver Bosede

Ii Sun Yoo

Systems and methods are provided for managing server loads that accounts for various measures of risk associated with different workloads assigned to servers. The systems and methods may include a memory storing instructions for server load management operations, and a processor configured to execute the stored instructions. The processor may receive a workload, determine a value associated with the workload indicating a predetermined importance of the workload, receive information for a plurality of active servers in a server cluster associated with the processor, determine risk levels associated with the active servers based on the received information, and assign the received workload to one of the active servers based on the determined value and the determined risk levels.

System and method for risk-aware server load balancing

Disclosed herein are system, method, and computer program product embodiments for generating labels for training a machine learning mode using an incremental time window process. The described process may be used in a recurrence detection system. A dataset may be analyzed using incremental split dates to divide the dataset into an analysis portion and a holdout portion. The analysis portion may be analyzed to determine input features related to a predicted recurrence in the dataset. The holdout portion may be tested against the analysis portion and the input features to generate a label. The label may indicate whether or not the holdout portion confirms the prediction. The testing of the holdout portion against the analysis portion may be repeated by incrementally using different split dates and multiple separate analysis portions and holdout portions to generate multiple labels and corresponding input features.

Incremental time window procedure for selecting training samples for a supervised learning algorithm

A method is disclosed, comprising: aggregating a plurality of sets of transactions, each set of transactions comprising transactions related to an account-merchant pairing; determining variables characterizing a recurrence period based on transaction dates of the transactions in the each set; predicting the recurrence period for a transaction related to the account-merchant pairing for each customer of the plurality of customers; aggregating another set of transactions between the plurality of customers and the merchant; evaluating a distribution of the recurrence period for each customer within range of a distant point; and based on the evaluation of the distribution of the recurrence period for each customer, generating a probability of the merchant having a recurrent transaction with the customer. The account-merchant pairing may comprise a customer account and a merchant of a plurality of customers and merchants. Another set of transactions may comprise transactions in the plurality of sets of transactions.

Technique to aggregate merchant level information for use in a supervised learning model to detect recurring trends in consumer transactions

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New York, NY, United States of America

Jeremy Gerstle