Systems and Methods for Implicit Rate-Constrained Optimization of Non-Decomposable Objectives

    公开(公告)号:US20220398506A1

    公开(公告)日:2022-12-15

    申请号:US17837398

    申请日:2022-06-10

    Applicant: Google LLC

    Abstract: A computer-implemented method for optimizing machine-learned models by non-decomposable objectives with improved performance includes obtaining data indicative of a plurality of machine-learned model parameters and at least one threshold comprising a machine-learned model; initializing an initial plurality of machine-learned model parameters and an initial at least one threshold such that the initial plurality of machine-learned model parameters and the initial at least one threshold satisfy a constraint function; determining a gradient of an objective function with respect to the plurality of machine-learned model parameters at a current optimization step based at least in part on an implicit function of the at least one threshold as a function of the plurality of machine-learned model parameters; and updating the plurality of machine-learned model parameters and the at least one threshold based at least in part on the gradient.

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