Patents by Inventor Celestine Mendler-Duenner

Celestine Mendler-Duenner has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11803779
    Abstract: In an approach for constructing an ensemble model from a set of base learners, a processor performs a plurality of boosting iterations, where: at each boosting iteration of the plurality of boosting iterations, a base learner is selected at random from a set of base learners, according to a sampling probability distribution of the set of base learners, and trained according to a training dataset; and the sampling probability distribution is altered: (i) after selecting a first base learner at a first boosting iteration of the plurality of boosting iterations and (ii) prior to selecting a second base learner at a final boosting iteration of the plurality of boosting iterations. A processor constructs an ensemble model based on base learners selected and trained during the plurality of boosting iterations.
    Type: Grant
    Filed: February 25, 2020
    Date of Patent: October 31, 2023
    Assignee: International Business Machines Corporation
    Inventors: Thomas Parnell, Andreea Anghel, Nikolas Ioannou, Nikolaos Papandreou, Celestine Mendler-Duenner, Dimitrios Sarigiannis, Charalampos Pozidis
  • Patent number: 11562270
    Abstract: Embodiments of the present invention provide computer-implemented methods, computer program products and systems. Embodiments of the present invention can run preemptable tasks distributed according to a distributed environment, wherein each task of a plurality of preemptable tasks has been assigned two or more of the training data samples to process during each iteration. Embodiments of the present invention can, upon verifying that a preemption condition for each iteration is satisfied: preempt any task of the preemptable tasks that have started processing training data samples assigned to it, and update the cognitive model based on outputs obtained from completed tasks, including outputs obtained from both the preempted tasks and completed tasks that have finished processing all training data samples as assigned to it.
    Type: Grant
    Filed: April 2, 2020
    Date of Patent: January 24, 2023
    Assignee: International Business Machines Corporation
    Inventors: Michael Kaufmann, Thomas Parnell, Antonios Kornilios Kourtis, Celestine Mendler-Duenner
  • Publication number: 20210312241
    Abstract: Embodiments of the present invention provide computer-implemented methods, computer program products and systems. Embodiments of the present invention can run preemptable tasks distributed according to a distributed environment, wherein each task of a plurality of preemptable tasks has been assigned two or more of the training data samples to process during each iteration. Embodiments of the present invention can, upon verifying that a preemption condition for each iteration is satisfied: preempt any task of the preemptable tasks that have started processing training data samples assigned to it, and update the cognitive model based on outputs obtained from completed tasks, including outputs obtained from both the preempted tasks and completed tasks that have finished processing all training data samples as assigned to it.
    Type: Application
    Filed: April 2, 2020
    Publication date: October 7, 2021
    Inventors: Michael Kaufmann, Thomas Parnell, Antonios Kornilios Kourtis, Celestine Mendler-Duenner
  • Publication number: 20210264320
    Abstract: In an approach for constructing an ensemble model from a set of base learners, a processor performs a plurality of boosting iterations, where: at each boosting iteration of the plurality of boosting iterations, a base learner is selected at random from a set of base learners, according to a sampling probability distribution of the set of base learners, and trained according to a training dataset; and the sampling probability distribution is altered: (i) after selecting a first base learner at a first boosting iteration of the plurality of boosting iterations and (ii) prior to selecting a second base learner at a final boosting iteration of the plurality of boosting iterations. A processor constructs an ensemble model based on base learners selected and trained during the plurality of boosting iterations.
    Type: Application
    Filed: February 25, 2020
    Publication date: August 26, 2021
    Inventors: Thomas Parnell, Andreea Anghel, Nikolas loannou, Nikolaos Papandreou, Celestine Mendler-Duenner, Dimitrios Sarigiannis, Charalampos Pozidis