公开(公告)号：US20210342732A1

公开(公告)日：2021-11-04

申请号：US16861671

申请日：2020-04-29

Applicant: International Business Machines Corporation

Inventor： Lior Horesh ,  Giacomo Nannicini ,  Oktay Gunluk ,  Sanjeeb Dash ,  Parikshit Ram ,  Alexander Gray

IPC: G06N20/00 ,  G06F17/10

Abstract: A processor may include a set of primitive operators, receive a set of data-driven operators, at least one of the set of data-driven operators including a machine learning model, and receive an input-output data pair set. Based on a grammar specifying rules for linking the set of primitive operators and the set of data-driven operators, the processor may search among the set of primitive operators and the set of data-driven operators to find a symbolic model that fits the input-output data set.

公开(公告)号：US20210304028A1

公开(公告)日：2021-09-30

申请号：US16832528

申请日：2020-03-27

Applicant: International Business Machines Corporation

Inventor： Daniel Karl I. Weidele ,  Parikshit Ram ,  Dakuo Wang ,  Abel Nicolas Valente ,  Arunima Chaudhary

IPC: G06N5/04 ,  G06N20/00

Abstract: Systems, computer-implemented methods, and computer program products to facilitate conditional parallel coordinates in automated artificial intelligence with constraints are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a visualization component that renders a pipeline constraint as a constraint axis having constraint scores of machine learning pipelines in a conditional parallel coordinates visualization. The computer executable components can further comprise a model generation component that generates a machine learning model based on the constraint scores of the machine learning pipelines.

公开(公告)号：US12242980B2

公开(公告)日：2025-03-04

申请号：US17015243

申请日：2020-09-09

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Parikshit Ram ,  Dakuo Wang ,  Deepak Vijaykeerthy ,  Vaibhav Saxena ,  Sijia Liu ,  Arunima Chaudhary ,  Gregory Bramble ,  Horst Cornelius Samulowitz ,  Alexander Gray

IPC: G06N5/04 ,  G06F9/38 ,  G06F18/243

Abstract: The exemplary embodiments disclose a method, a computer program product, and a computer system for determining that one or more model pipelines satisfy one or more constraints. The exemplary embodiments may include detecting a user uploading data, one or more constraints, and one or more model pipelines, collecting the data, the one or more constraints, and the one or more model pipelines, and determining that one or more of the model pipelines satisfies all of the one or more constraints based on applying one or more algorithms to the collected data, constraints, and model pipelines.

公开(公告)号：US20240144027A1

公开(公告)日：2024-05-02

申请号：US18175006

申请日：2023-02-27

Applicant: International Business Machines Corporation

Inventor： Yuya Jeremy Ong ,  Yi Zhou ,  Parikshit Ram ,  Theodoros Salonidis ,  Nathalie Baracaldo Angel

IPC: G06N3/098

CPC classification number: G06N3/098

Abstract: A method, a computer program product, and a system of personalized training a machine learning model using federated learning with gradient boosted trees. The method includes training a global machine learning model using federated learning between a plurality of parties. The method also includes distributing the global machine learning model to each of the parties and receiving personalized model updates from each of the parties. The personalized model updates are generated from updated models boosted locally and produced by each of the parties using their respective local data. The method further includes fusing the personalized model updates to produce a boosted decision tree to update the global machine learning model. The method also includes training global machine learning model, iteratively, in this manner until a stopping criterion is achieved.

公开(公告)号：US11681931B2

公开(公告)日：2023-06-20

申请号：US16580953

申请日：2019-09-24

Applicant: International Business Machines Corporation

Inventor： Bo Zhang ,  Gregory Bramble ,  Parikshit Ram ,  Horst Cornelius Samulowitz

IPC: G06N5/04 ,  G06N20/00

CPC classification number: G06N5/04 ,  G06N20/00

Abstract: A system that provides a mathematical formulation for new problem of model validation and model selection in presence of test data feedback. The system comprises a memory that stores computer-executable components. A processor, operably coupled to the memory, executes the computer-executable components stored in the memory. A selection component selects a metric of performance evaluation accuracy; and a configuration component configures performance evaluation schemes for machine learning algorithms. A characterization component employs a supervised learning-based approach to characterize relationship between the configuration of the performance evaluation scheme and fidelity of performance estimates; and an optimization component that optimizes accuracy of the machine learning algorithms as a function of size of training data set relative to size of validation data set through selection of values associated with the configuration parameters.

公开(公告)号：US20210326736A1

公开(公告)日：2021-10-21

申请号：US16851775

申请日：2020-04-17

Applicant: International Business Machines Corporation

Inventor： Akihiro Kishimoto ,  Djallel Boundeffouf ,  Bei Chen ,  Radu Marinescu ,  Parikshit Ram ,  Ambrish Rwat ,  Martin Wistuba

IPC: G06N7/00 ,  G06N20/00 ,  G06K9/62 ,  G06F9/38

Abstract: Systems, computer-implemented methods, and computer program products to facilitate automated generation of a machine learning pipeline based on a pipeline grammar are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a pipeline structure generator component that generates a machine learning pipeline structure based on a pipeline grammar. The computer executable components can further comprise a pipeline optimizer component that selects one or more machine learning modules that achieve a defined objective to instantiate a machine learning pipeline based on the machine learning pipeline structure.

公开(公告)号：US20250123896A1

公开(公告)日：2025-04-17

申请号：US18488982

申请日：2023-10-17

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Thomas Parnell ,  Malgorzata Lazuka ,  Andreea Anghel ,  Parikshit Ram

IPC: G06F9/50 ,  G06F9/445

Abstract: A computer product and methodology for serving a cloud workload across multiple cloud service providers. A first evaluation is performed for a first configuration in a first cloud service of the plurality of cloud services, and a second evaluation is performed for a second configuration in a second cloud service of the plurality of cloud services. A first result of the first evaluation and a second result of the second evaluation are used to select an unevaluated configuration in one of the first and second cloud services for performing another evaluation.

公开(公告)号：US12112249B2

公开(公告)日：2024-10-08

申请号：US17115673

申请日：2020-12-08

Applicant: International Business Machines Corporation

Inventor： Vaibhav Saxena ,  Aswin Kannan ,  Saurabh Manish Raje ,  Parikshit Ram ,  Yogish Sabharwal ,  Ashish Verma

IPC: G06N20/00 ,  G06N3/006

CPC classification number: G06N3/006 ,  G06N20/00

Abstract: A system, computer program product, and method are presented for performing multi-objective automated machine learning, and, more specifically, to identifying a plurality of machine learning pipelines as Pareto-optimal solutions to optimize a plurality of objectives. The method includes receiving input data directed toward one or more subjects of interest and determining a plurality of objectives to be optimized. The method also includes ingesting at least a portion of the input data through one or more machine learning (ML) models. The method further includes aggregating the plurality of objectives into one or more aggregated single objectives. The method also includes determining a plurality of Pareto-optimal solutions, thereby defining a plurality of ML pipelines that optimize the one or more aggregated single objectives. The method further includes selecting one ML pipeline from the plurality of ML pipelines.

公开(公告)号：US20220188691A1

公开(公告)日：2022-06-16

申请号：US17119134

申请日：2020-12-11

Applicant: International Business Machines Corporation

Inventor： Michael Katz ,  Parikshit Ram ,  Shirin Sohrabi Araghi ,  Octavian Udrea

IPC: G06N20/00

Abstract: The present disclosure includes a computer implemented method, system, and computer program product for automated generation of trained machine learning models and a machine learning model created using the method. The method may comprise receiving a space of possible automatically generated trained machine learning model pipelines, the space defined by a context-free grammar, generating, by a processor, a planning model from the context-free grammar, and automatically generating, by the processor, a plurality of candidate trained machine learning pipelines based upon the planning model.

公开(公告)号：US11074728B2

公开(公告)日：2021-07-27

申请号：US16676410

申请日：2019-11-06

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Daniel Karl I. Weidele ,  Parikshit Ram

IPC: G09G5/00 ,  G06T11/20 ,  G06T3/20

Abstract: A conditional parallel coordinate visualization system is provided. The system presents a parallel coordinate visualization that includes a set of parallel main axes that respectively correspond to a set of main dimensions. The system receives a first multivariate data including values at the set of main dimensions. The first multivariate data has a first additional data that includes values in a first set of sub-dimensions. The first set of sub-dimensions is associated with a first predicate value at a first predicate dimension in the set of main dimensions. The system presents the first multivariate data as a polyline that intersects the set of parallel main axes. Upon a selection of an option item, the system unfolds the parallel coordinate visualization to reveal a first set of parallel sub-axes that correspond to the first set of sub-dimensions. The system presents the first additional data at the first set of parallel sub-axes.