Abstract: A processor receives requests for content items and identifies a first subset of machine learning (ML) models that satisfy a reliability criterion and a second subset of ML models that fail to satisfy the reliability criterion, wherein each ML model is associated with a respective content template and is trained to output a probability that a target associated with an input set of characteristics would perform a target action responsive to being presented with a content item generated based on the respective associated content template. The processing logic assigns each request to either a first group or a second group based on a ratio of a number of ML models in the first subset to a number of ML models in the second subset. For each request in the first group, the processor generates a content item based on a content template associated with the first subset.

Abstract: A processor receives requests for content items and identifies a first subset of machine learning (ML) models that satisfy a reliability criterion and a second subset of ML models that fail to satisfy the reliability criterion, wherein each ML model is associated with a respective content template and is trained to output a probability that a target associated with an input set of characteristics would perform a target action responsive to being presented with a content item generated based on the respective associated content template. For each request in a first group, the processor inputs the respective set of characteristics associated with the request into each ML model of the first subset, selects a content template, and generates a content item based on the selected content template. For each request in the second group, the processor generates a content item based on a content template associated with the second subset.

Abstract: A method of machine learning includes performing dimensionality reduction on a parameter space by performing initial tests to determine scores for a plurality of parameter values in the parameter space, determining aggregate scores for a plurality of parameter value combinations, determining a ranking of the plurality of parameter value combinations based on the aggregate scores, and performing cluster analysis on the plurality of parameter value combinations to determine a set having highest aggregate scores. The method further includes performing additional tests, wherein each additional test is for a parameter value combination in the set. For each such parameter value combination, a probability of achieving a key performance indicator (KPI) is computed. Cluster analysis is then performed to determine a first subset of the set having highest probabilities of achieving the KPI. An operation is then performed on the first subset.

Abstract: A processor receives requests for content items and identifies a first subset of machine learning (ML) models that satisfy a reliability criterion and a second subset of ML models that fail to satisfy the reliability criterion, wherein each ML model is associated with a respective content template and is trained to output a probability that a target associated with an input set of characteristics would perform a target action responsive to being presented with a content item generated based on the respective associated content template. For each request in a first group, the processor inputs the respective set of characteristics associated with the request into each ML model of the first subset, selects a content template, and generates a content item based on the selected content template. For each request in the second group, the processor generates a content item based on a content template associated with the second subset.

Abstract: A method of machine learning includes performing dimensionality reduction on a parameter space by performing initial tests to determine scores for a plurality of parameter values in the parameter space, determining aggregate scores for a plurality of parameter value combinations, determining a ranking of the plurality of parameter value combinations based on the aggregate scores, and performing cluster analysis on the plurality of parameter value combinations to determine a set having highest aggregate scores. The method further includes performing additional tests, wherein each additional test is for a parameter value combination in the set. For each such parameter value combination, a probability of achieving a key performance indicator (KPI) is computed. Cluster analysis is then performed to determine a first subset of the set having highest probabilities of achieving the KPI. An operation is then performed on the first subset.

Abstract: A method of machine learning includes performing dimensionality reduction on a parameter space by performing initial tests to determine scores for a plurality of parameter values in the parameter space, determining aggregate scores for a plurality of parameter value combinations, determining a ranking of the plurality of parameter value combinations based on the aggregate scores, and performing cluster analysis on the plurality of parameter value combinations to determine a set having highest aggregate scores. The method further includes performing additional tests, wherein each additional test is for a parameter value combination in the set. For each such parameter value combination, a probability of achieving a key performance indicator (KPI) is computed. Cluster analysis is then performed to determine a first subset of the set having highest probabilities of achieving the KPI. An operation is then performed on the first subset.

Abstract: A method of machine learning includes performing dimensionality reduction on a parameter space by performing initial tests to determine scores for a plurality of parameter values in the parameter space, determining aggregate scores for a plurality of parameter value combinations, determining a ranking of the plurality of parameter value combinations based on the aggregate scores, and performing cluster analysis on the plurality of parameter value combinations to determine a set having highest aggregate scores. The method further includes performing additional tests, wherein each additional test is for a parameter value combination in the set. For each such parameter value combination, a probability of achieving a key performance indicator (KPI) is computed. Cluster analysis is then performed to determine a first subset of the set having highest probabilities of achieving the KPI. An operation is then performed on the first subset.

Abstract: A method of machine learning includes performing dimensionality reduction on a parameter space by performing initial tests to determine scores for a plurality of parameter values in the parameter space, determining aggregate scores for a plurality of parameter value combinations, determining a ranking of the plurality of parameter value combinations based on the aggregate scores, and performing cluster analysis on the plurality of parameter value combinations to determine a set having highest aggregate scores. The method further includes performing additional tests, wherein each additional test is for a parameter value combination in the set. For each such parameter value combination, a probability of achieving a key performance indicator (KPI) is computed. Cluster analysis is then performed to determine a first subset of the set having highest probabilities of achieving the KPI. An operation is then performed on the first subset.

Abstract: A method and computer program product for accessing a secure resource using a certificate bound with authentication information. In one implementation, the method includes receiving a certificate request from a user, the certificate request including identification information and authentication information associated with the user; verifying the identification information; issuing a certificate to the user when the identification information is verified; and sending the authentication information and a certificate identifier for the certificate to an authentication server. According to one aspect, the sending step includes signing a combination of the authentication information and the certificate identifier to form a unique user identifier; signing the authentication information; and sending the unique user identifier to the authentication server.

Abstract: Techniques for providing secure transactions can include receiving a request for access to a first server by a user. The request includes the user's credentials such as biometric information, an electronic certificate, or other information. The user is authenticated based on the credentials, and a token is sent to the first server. The token indicates whether the user has been authenticated and includes criteria about the user. Based on the criteria in the token, the first server can determine whether the user is authorized to perform a particular transaction in connection with a specified file or application at the first server. The user can be re-authenticated prior to allowing the transaction to be completed. Each time the user is authenticated, a time-stamped record can be stored. Encryption can be used to enhance security.