Patents Assigned to Unlearn.AI, Inc.
  • Publication number: 20220157413
    Abstract: Systems and methods for designing random control trials in accordance with embodiments of the invention are illustrated. One embodiment includes a method for designing a target random control trial. The method includes steps for generating a set of prognostic scores for a set of samples, computing a first correlation between the set of prognostic scores and a set of outcomes for the set of samples, computing a first variance for the set of outcomes for the set of samples, estimating a second correlation and a second variance for a target random control trial, and determining a set of target trial parameters based on the first and second correlations and the first and second variances.
    Type: Application
    Filed: February 1, 2022
    Publication date: May 19, 2022
    Applicant: Unlearn.AI, Inc.
    Inventors: Charles Kenneth Fisher, Aaron Michael Smith, Jonathan Ryan Walsh, Alejandro Schuler da Costa Ferro, David Walsh, David Putnam Miller
  • Publication number: 20210117842
    Abstract: Systems and methods for training and utilizing constrained generative models in accordance with embodiments of the invention are illustrated. One embodiment includes a method for training a constrained generative model. The method includes steps for receiving a set of data samples from a first distribution, identifying a set of constraints from a second distribution, and training a generative model based on the set of data samples and the set of constraints.
    Type: Application
    Filed: October 19, 2020
    Publication date: April 22, 2021
    Applicant: Unlearn.AI, Inc.
    Inventors: Aaron M. Smith, Anton D. Loukianov, Charles K. Fisher, Jonathan R. Walsh
  • Publication number: 20190220733
    Abstract: Systems and methods for modeling complex probability distributions are described. One embodiment includes a method for training a restricted Boltzmann machine (RBM), wherein the method includes generating, from a first set of visible values, a set of hidden values in a hidden layer of a RBM and generating a second set of visible values in a visible layer of the RBM based on the generated set of hidden values. The method includes computing a set of likelihood gradients based on the first set of visible values and the generated set of visible values, computing a set of adversarial gradients using an adversarial model based on at least one of the set of hidden values and the set of visible values, computing a set of compound gradients based on the set of likelihood gradients and the set of adversarial gradients, and updating the RBM based on the set of compound gradients.
    Type: Application
    Filed: January 16, 2019
    Publication date: July 18, 2019
    Applicant: Unlearn.AI, Inc.
    Inventors: Charles Kenneth Fisher, Aaron Michael Smith, Jonathan Ryan Walsh