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公开(公告)号：US11544767B2
公开(公告)日：2023-01-03
申请号：US17715214
申请日：2022-04-07
Applicant: SAS Institute Inc.
Inventor： Xuejun Liao , Patrick Nathan Koch
Abstract: A computing device determines a recommendation. A confidence matrix is computed using a predefined weight value. (A) A first parameter matrix is updated using the confidence matrix, a predefined response matrix, a first step-size parameter value, and a first direction matrix. The predefined response matrix includes a predefined response value by each user to each item and at least one matrix value for which a user has not provided a response to an item. (B) A second parameter matrix is updated using the confidence matrix, the predefined response matrix, a second step-size parameter value, and a second direction matrix. (C) An objective function value is updated based on the first and second parameter matrices. (D) The first and second parameter matrices are trained by repeating (A) through (C). The first and second parameter matrices output for use in predicting a recommended item for a requesting user.
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公开(公告)号：US11093833B1
公开(公告)日：2021-08-17
申请号：US17081118
申请日：2020-10-27
Applicant: SAS Institute Inc.
Inventor： Steven Joseph Gardner , Joshua David Griffin , Yan Xu , Patrick Nathan Koch , Brett Alan Wujek , Oleg Borisovich Golovidov
Abstract: Tuned hyperparameter values are determined for training a machine learning model. When a selected hyperparameter configuration does not satisfy a linear constraint, if a projection of the selected hyperparameter configuration is included in a first cache that stores previously computed projections is determined. When the projection is included in the first cache, the projection is extracted from the first cache using the selected hyperparameter configuration, and the selected hyperparameter configuration is replaced with the extracted projection in the plurality of hyperparameter configurations. When the projection is not included in the first cache, a projection computation for the selected hyperparameter configuration is assigned to a session. A computed projection is received from the session for the selected hyperparameter configuration. The computed projection and the selected hyperparameter configuration are stored to the first cache, and the selected hyperparameter configuration is replaced with the computed projection.
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公开(公告)号：US20250068927A1
公开(公告)日：2025-02-27
申请号：US18583837
申请日：2024-02-21
Applicant: SAS INSTITUTE INC.
Inventor： Xuejun Liao , Patrick Nathan Koch
IPC: G06N20/00
Abstract: A system, method, and computer-program product includes receiving an input comprising a plurality of pre-defined factor matrices and an implicit feedback dataset partitioned into a plurality of implicit feedback data subsets; distributing the input across a controller node and a plurality of worker nodes implemented in a distributed computing environment; and training a model using the controller node and the plurality of worker nodes, wherein training the model includes: initializing, by the controller node, a controller-specific user parameters matrix and a controller-specific item parameters matrix, broadcasting, by the controller node, the controller-specific user parameters matrix and the controller-specific item parameters matrix to each worker node of the plurality of worker nodes, and concurrently executing an aggregation model training algorithm at the controller node and a plurality of localized model training algorithms across the plurality of worker nodes until a training termination condition is satisfied.
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公开(公告)号：US20220237685A1
公开(公告)日：2022-07-28
申请号：US17715214
申请日：2022-04-07
Applicant: SAS Institute Inc.
Inventor： Xuejun Liao , Patrick Nathan Koch
Abstract: A computing device determines a recommendation. A confidence matrix is computed using a predefined weight value. (A) A first parameter matrix is updated using the confidence matrix, a predefined response matrix, a first step-size parameter value, and a first direction matrix. The predefined response matrix includes a predefined response value by each user to each item and at least one matrix value for which a user has not provided a response to an item. (B) A second parameter matrix is updated using the confidence matrix, the predefined response matrix, a second step-size parameter value, and a second direction matrix. (C) An objective function value is updated based on the first and second parameter matrices. (D) The first and second parameter matrices are trained by repeating (A) through (C). The first and second parameter matrices output for use in predicting a recommended item for a requesting user.
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公开(公告)号：US20220138605A1
公开(公告)日：2022-05-05
申请号：US17386853
申请日：2021-07-28
Applicant: SAS Institute Inc.
Inventor： Xuejun Liao , Patrick Nathan Koch , Shunping Huang , Yan Xu
Abstract: A computing device determines a recommendation. (A) A first parameter matrix is updated using a first direction matrix and a first step-size parameter value that is greater than one. The first parameter matrix includes a row dimension equal to a number of users of a plurality of users included in a ratings matrix and the ratings matrix includes a missing matrix value. (B) A second parameter matrix is updated using a second direction matrix and a second step-size parameter value that is greater than one. The second parameter matrix includes a column dimension equal to a number of items of a plurality of items included in the ratings matrix. (C) An objective function value is updated based on the first parameter matrix and the second parameter matrix. (D) (A) through (C) are repeated until the first parameter matrix and the second parameter matrix satisfy a convergence test.
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Patent Agency Ranking
公开(公告)号：US20250103579A1
公开(公告)日：2025-03-27
申请号：US18912220
申请日：2024-10-10
Applicant: SAS Institute Inc.
Inventor： Yongqiao Xiao , Mary Elizabeth Carter , Arash Dehghan Banadaki , Avery Winston Acierno , Patrick Nathan Koch
Abstract: In one example, a system can receive, from application code including an analysis operation performed on a set of data, an indication to access the set of data included in a tabular data structure using an application programming interface (API), in which the tabular data structure is associated with a memory allocation and a type. The system can determine that the type of the tabular data structure is the native type, the native type characterizing data structures that are accessed using a first programming language and a second programming language. The system can identify a proxy data table that shares the memory allocation, the proxy data table accessed using the API based on the second programming language. The system can issue one or more read commands to the proxy data table to cause the set of data to be read from the tabular data structure.
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公开(公告)号：US12259867B1
公开(公告)日：2025-03-25
申请号：US18911810
申请日：2024-10-10
Applicant: SAS Institute Inc.
Inventor： Yongqiao Xiao , Mary Elizabeth Carter , Arash Dehghan Banadaki , Avery Winston Acierno , Patrick Nathan Koch
Abstract: In one example, a system can receive information about a tabular data structure in a memory including a set of data and a first memory allocation. The system can determine a type of the tabular data structure, the type selected from among two types including a native type and a non-native type. The system can, in response to the type being the native type, identify a first proxy data table usable as a proxy for the tabular data structure that shares the first memory allocation. The system can receive a first indication to access the set of data from application code. The system can issue one or more first read commands to the first proxy data table to cause the set of data to be read from the tabular data structure.
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公开(公告)号：US12141138B1
公开(公告)日：2024-11-12
申请号：US18599342
申请日：2024-03-08
Applicant: SAS Institute Inc.
Inventor： Yongqiao Xiao , Patrick Nathan Koch
IPC: G06F16/2452 , G06F8/41
Abstract: In one example, a system can receive information about a data structure including a set of data entries. The system can generate a proxy data table including a set of columns. The system can use a data access layer to generate a mapping from the data entries to the columns. The system can receive an input to cause an operation to be performed on the data structure by performing the operation on the data structure. Generating a result can involve issuing read commands to the data access layer to perform the operation on the data structure such that the data access layer obtains the associated data entries and provides them as responses to the read commands by performing a translation between the data entries and the columns based on the mapping. The system can then output the result of the operation.
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公开(公告)号：US11379743B2
公开(公告)日：2022-07-05
申请号：US17386853
申请日：2021-07-28
Applicant: SAS Institute Inc.
Inventor： Xuejun Liao , Patrick Nathan Koch , Shunping Huang , Yan Xu
Abstract: A computing device determines a recommendation. (A) A first parameter matrix is updated using a first direction matrix and a first step-size parameter value that is greater than one. The first parameter matrix includes a row dimension equal to a number of users of a plurality of users included in a ratings matrix and the ratings matrix includes a missing matrix value. (B) A second parameter matrix is updated using a second direction matrix and a second step-size parameter value that is greater than one. The second parameter matrix includes a column dimension equal to a number of items of a plurality of items included in the ratings matrix. (C) An objective function value is updated based on the first parameter matrix and the second parameter matrix. (D) (A) through (C) are repeated until the first parameter matrix and the second parameter matrix satisfy a convergence test.
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公开(公告)号：US20180240041A1
公开(公告)日：2018-08-23
申请号：US15822462
申请日：2017-11-27
Applicant: SAS Institute Inc.
Inventor： Patrick Nathan Koch , Brett Alan Wujek , Oleg Borisovich Golovidov , Steven Joseph Gardner , Joshua David Griffin , Scott Russell Pope , Yan Xu
Abstract: A computing device automatically selects hyperparameter values based on objective criteria to train a predictive model. Each session of a plurality of sessions executes training and scoring of a model type using an input dataset in parallel with other sessions of the plurality of sessions. Unique hyperparameter configurations are determined using a search method and assigned to each session. For each session of the plurality of sessions, training of a model of the model type is requested using a training dataset and the assigned hyperparameter configuration, scoring of the trained model using a validation dataset and the assigned hyperparameter configuration is requested to compute an objective function value, and the received objective function value and the assigned hyperparameter configuration are stored. A best hyperparameter configuration is identified based on an extreme value of the stored objective function values.
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