Patents by Inventor Cedric Philippe Archambeau

Cedric Philippe Archambeau 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: 12333438
    Abstract: A particular hyper-parameter combination (HPC) that was recommended for a first task is included in a collection of candidate HPCs evaluated for a second task. Hyper-parameter analysis iterations are conducted for the second task using the collection. In one of the iterations, the second task is executed using a first iteration-specific set of HPCs, including the particular HPC and one or more other members of the collection. One or more of the HPCs of the first iteration-specific set of HPCs are pruned to generate a second iteration-specific set of HPCs for a subsequent iteration. HPCs are selected for pruning based on a comparison of their results with the results obtained from the particular HPC that was recommended for the first task. A recommended HPC for the second task is identified based on results of the analysis iterations.
    Type: Grant
    Filed: June 30, 2021
    Date of Patent: June 17, 2025
    Assignee: Amazon Technologies, Inc.
    Inventors: Giovanni Zappella, Cedric Philippe Archambeau, David Salinas
  • Publication number: 20250013899
    Abstract: Hyperparameters for tuning a machine learning system may be optimized using Bayesian optimization with constraints. The hyperparameter optimization may be performed for a received training set and received constraints. Respective probabilistic models for the machine learning system and constraint functions may be initialized, then hyperparameter optimization may include iteratively identifying respective values for hyperparameters using analysis of the respective models performed using an acquisition function implementing entropy search on the respective models, training the machine learning system using the identified values to determine measures of accuracy and constraint metrics, and updating the respective models using the determined measures.
    Type: Application
    Filed: September 17, 2024
    Publication date: January 9, 2025
    Applicant: Amazon Technologies, Inc.
    Inventors: Giovanni Zappella, Valerio Perrone, Iaroslav Shcherbatyi, Rodolphe Jenatton, Cedric Philippe Archambeau, Matthias Seeger
  • Patent number: 12165082
    Abstract: Hyperparameters for tuning a machine learning system may be optimized using Bayesian optimization with constraints. The hyperparameter optimization may be performed for a received training set and received constraints. Respective probabilistic models for the machine learning system and constraint functions may be initialized, then hyperparameter optimization may include iteratively identifying respective values for hyperparameters using analysis of the respective models performed using an acquisition function implementing entropy search on the respective models, training the machine learning system using the identified values to determine measures of accuracy and constraint metrics, and updating the respective models using the determined measures.
    Type: Grant
    Filed: June 29, 2020
    Date of Patent: December 10, 2024
    Assignee: Amazon Technologies, Inc.
    Inventors: Giovanni Zappella, Valerio Perrone, Iaroslav Shcherbatyi, Rodolphe Jenatton, Cedric Philippe Archambeau, Matthias Seeger
  • Publication number: 20240232526
    Abstract: A determination is made that an explanatory data set for a common set of predictions generated by a machine learning model for records containing text tokens is to be provided. Respective groups of related tokens are identified from the text attributes of the records, and record-level prediction influence scores are generated for the token groups. An aggregate prediction influence score is generated for at least some of the token groups from the record-level scores, and an explanatory data set based on the aggregate scores is presented.
    Type: Application
    Filed: March 19, 2024
    Publication date: July 11, 2024
    Applicant: Amazon Technologies, Inc.
    Inventors: Cedric Philippe Archambeau, Sanjiv Ranjan Das, Michele Donini, Michaela Hardt, Tyler Stephen Hill, Krishnaram Kenthapadi, Pedro L Larroy, Xinyu Liu, Keerthan Harish Vasist, Pinar Altin Yilmaz, Muhammad Bilal Zafar
  • Patent number: 11977836
    Abstract: A determination is made that an explanatory data set for a common set of predictions generated by a machine learning model for records containing text tokens is to be provided. Respective groups of related tokens are identified from the text attributes of the records, and record-level prediction influence scores are generated for the token groups. An aggregate prediction influence score is generated for at least some of the token groups from the record-level scores, and an explanatory data set based on the aggregate scores is presented.
    Type: Grant
    Filed: November 26, 2021
    Date of Patent: May 7, 2024
    Assignee: Amazon Technologies, Inc.
    Inventors: Cedric Philippe Archambeau, Sanjiv Ranjan Das, Michele Donini, Michaela Hardt, Tyler Stephen Hill, Krishnaram Kenthapadi, Pedro L Larroy, Xinyu Liu, Keerthan Harish Vasist, Pinar Altin Yilmaz, Muhammad Bilal Zafar
  • Publication number: 20240112011
    Abstract: A system and method for continual learning in a provider network. The method is configured to implement or interface with a system which implements a semi-automated or fully automated architecture of continual machine learning, the semi-automated or fully automated architecture implementing user-configurable model retraining or hyperparameter tuning, which is enabled by a provider network. This functions to adapt a model over time to new information in the training data while also providing a user-friendly, flexible, and customizable continual learning process.
    Type: Application
    Filed: September 30, 2022
    Publication date: April 4, 2024
    Inventors: Giovanni ZAPPELLA, Lukas Stefan BALLES, Beyza ERMIS, Martin WISTUBA, Cedric Philippe ARCHAMBEAU
  • Patent number: 11481659
    Abstract: Hyperparameters for tuning a machine learning system may be optimized for fairness using Bayesian optimization with constraints for accuracy and bias. Hyperparameter optimization may be performed for a received training set and received accuracy and fairness constraints. Respective probabilistic models for accuracy and bias of the machine learning system may be initialized, then hyperparameter optimization may include iteratively identifying respective values for hyperparameters using analysis of the respective models performed using an acquisition function implementing constrained expected improvement on the respective models, training the machine learning system using the identified values to determine measures of accuracy and bias, and updating the respective models using the determined measures.
    Type: Grant
    Filed: June 30, 2020
    Date of Patent: October 25, 2022
    Assignee: Amazon Technologies, Inc.
    Inventors: Valerio Perrone, Michele Donini, Krishnaram Kenthapadi, Cedric Philippe Archambeau
  • Patent number: 10853735
    Abstract: Systems, methods, and computer-readable media are disclosed for maximizing quantifiable user interaction via modification of adjustable parameters. In one embodiment, an example method may include determining a first output to maximize, where the first output is a function of a first adjustable parameter and a second adjustable parameter, determining first data comprising a first actual value of the first output when the first adjustable parameter is set to a first value and the second adjustable parameter is set to a second value, and determining a first predictive model that generates a first predicted value of the first output. Example methods may include determining, using the first predictive model, a third value for the first adjustable parameter and a fourth value for the second adjustable parameter to maximize the first predicted value, and sending the third value and the fourth value.
    Type: Grant
    Filed: June 6, 2016
    Date of Patent: December 1, 2020
    Assignee: Amazon Technologies, Inc.
    Inventors: Yu Gan, Cédric Philippe Archambeau, Rodolphe Jenatton, Jim Huang, Fabian Lutz-Frank Wauthier