Alan Thomas Mackey

US Patent:

20190266513, Aug 29, 2019

Filed:

Feb 28, 2019

Appl. No.:

16/288217

Inventors:

- Mountain View CA, US
Alan Mackey - Mountain View CA, US
Xiyang Luo - Pasadena CA, US

International Classification:

G06N 20/00

Abstract:

Example aspects of the present disclosure are directed to systems and methods for learning classification models which satisfy constraints such as, for example, constraints that can be expressed as a predicted positive rate or negative rate on a subset of the training dataset. In particular, through the use of quantile estimators, the systems and methods of the present disclosure can transform a constrained optimization problem into an unconstrained optimization problem that is solved more efficiently and generally than the constrained optimization problem. As one example, the unconstrained optimization problem can include optimizing an objective function where a decision threshold of the classification model is expressed as an estimator of a quantile function on the classification scores of the machine-learned classification model for a subset of the training dataset at a desired quantile.