公开(公告)号：US20250045611A1

公开(公告)日：2025-02-06

申请号：US18751509

申请日：2024-06-24

Applicant: SAS Institute Inc.

Inventor： Xilong Chen ,  Tao Huang ,  Jan Chvosta

IPC: G06N7/01 ,  G06F17/16 ,  G06N20/00

Abstract: A computing device learns a directed acyclic graph for a plurality of variables. (A) A target variable and zero or more input variables are defined based on a predefined topological order vector and a first index. (B) A machine learning model is trained with observation vectors using the target variable and the input variables. (C) The machine learning model is executed using the observation vectors with the target variable and the input variables to compute a residual vector. (D) The first index is incremented. (E) (A) through (D) are repeated a first plurality of times. A parent set is determined for each variable by comparing the residual vector computed each repetition of (C) to other residual vectors computed on other repetitions of (C). The parent set is output for each variable to describe a directed acyclic graph that defines a hierarchical relationship between the variables.

公开(公告)号：US20250045355A1

公开(公告)日：2025-02-06

申请号：US18751584

申请日：2024-06-24

Applicant: SAS Institute Inc.

Inventor： Xilong Chen ,  Tao Huang ,  Jan Chvosta

IPC: G06F17/18

Abstract: A computing device learns a directed acyclic graph (DAG). (A) A target variable is defined from variables based on a topological order vector and a first index. (B) Input variables are defined from the variables based on the topological order vector and a second index. (C) A machine learning model is trained with observation vectors using the target variable and the input variables. (D) The machine learning model is executed to compute a loss value. (E) The second index is incremented. (F) (B) through (E) are repeated a first plurality of times. (G) The first index is incremented. (H) (A) through (G) are repeated a second plurality of times. A parent set is determined for each variable based on a comparison between the loss value computed each repetition of (D). The parent set is output for each variable to describe the DAG that defines a hierarchical relationship between the variables.

公开(公告)号：US20250045263A1

公开(公告)日：2025-02-06

申请号：US18538066

申请日：2023-12-13

Applicant: SAS Institute Inc.

Inventor： Xilong Chen ,  Tao Huang ,  Jan Chvosta

IPC: G06F16/22 ,  G06N3/04 ,  G06N3/08

Abstract: A computing device learns a best topological order vector for a plurality of variables. (A) A topological order vector is defined. (B) A target variable and zero or more input variables are defined based on the topological order vector. (C) A machine learning model is trained with observation vectors using values of the target variable and the zero or more input variables. (D) The machine learning model is executed with second observation vectors using the values of the target variable and the zero or more input variables to compute a loss value. (E) (A) through (D) are repeated a plurality of times. Each topological order vector defined in (A) is unique in comparison to other topological order vectors defined in (A). The best topological order vector is determined based on a comparison between the loss values computed for each topological order vector in (D).

公开(公告)号：US12056207B1

公开(公告)日：2024-08-06

申请号：US18538070

申请日：2023-12-13

Applicant: SAS Institute Inc.

Inventor： Xilong Chen ,  Tao Huang ,  Jan Chvosta

IPC: G06F17/18

CPC classification number: G06F17/18

Abstract: A computing device learns a best topological order vector of a plurality of variables. A target variable and zero or more input variables are defined. (A) A machine learning model is trained with observation vectors using the target variable and the zero or more input variables. (B) The machine learning model is executed to compute an equation loss value. (C) The equation loss value is stored with the identifier. (D) The identifier is incremented. (E) (A) through (D) are repeated a plurality of times. (F) A topological order vector is defined. (G) A loss value is computed from a subset of the stored equation loss values based on the topological order vector. (F) through (G) are repeated for each unique permutation of the topological order vector. A best topological order vector is determined based on a comparison between the loss value computed for each topological order vector in (G).

公开(公告)号：US11443198B1

公开(公告)日：2022-09-13

申请号：US17522062

申请日：2021-11-09

Applicant: SAS Institute Inc.

Inventor： Xilong Chen ,  Tao Huang ,  Jan Chvosta

IPC: G06N5/02 ,  G06F17/16

Abstract: A computing device learns a directed acyclic graph (DAG). An SSCP matrix is computed from variable values defined for observation vectors. A topological order vector is initialized that defines a topological order for the variables. A loss value is computed using the topological order vector and the SSCP matrix. (A) A neighbor determination method is selected. (B) A next topological order vector is determined relative to the initialized topological order vector using the neighbor determination method. (C) A loss value is computed using the next topological order vector and the SSCP matrix. (D) (B) and (C) are repeated until each topological order vector is determined in (B) based on the neighbor determination method. A best topological vector is determined from each next topological order vector based on having a minimum value for the computed loss value. An adjacency matrix is computed using the best topological vector and the SSCP matrix.

公开(公告)号：US20250053615A1

公开(公告)日：2025-02-13

申请号：US18905480

申请日：2024-10-03

Applicant: SAS Institute Inc.

Inventor： Xilong Chen ,  Tao Huang ,  Jan Chvosta

IPC: G06F17/18

Abstract: A computing device learns a directed acyclic graph (DAG). (A) A target variable is defined from variables based on a topological order vector and a first index. (B) Input variables are defined from the variables based on the topological order vector and a second index. (C) A machine learning model is trained with observation vectors using the target variable and the input variables. (D) The machine learning model is executed to compute a loss value. (E) The second index is incremented. (F) (B) through (E) are repeated a first plurality of times. (G) The first index is incremented. (H) (A) through (G) are repeated a second plurality of times. A parent set is determined for each variable based on a comparison between the loss value computed each repetition of (D). The parent set is output for each variable to describe the DAG that defines a hierarchical relationship between the variables.