Patents by Inventor Parikshit Ram
Parikshit Ram has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240144026Abstract: A computer-implemented method, according to one approach, includes issuing a hyperparameter optimization (HPO) query to a plurality of computing devices. HPO results are received from the plurality of computing devices, and the HPO results include a set of hyperparameter (HP)/rank value pairs. The method further includes computing, based on the set of HP/rank value pairs, a global set of HPs from the HPO results for federated learning (FL) training. An indication of the global set of HPs is output to the plurality of computing devices. A computer program product, according to another approach, includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a computer to cause the computer to perform the foregoing method.Type: ApplicationFiled: February 28, 2023Publication date: May 2, 2024Inventors: Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo Angel, Horst Cornelius Samulowitz, Heiko H. Ludwig
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Publication number: 20240144027Abstract: 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.Type: ApplicationFiled: February 27, 2023Publication date: May 2, 2024Inventors: Yuya Jeremy Ong, Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo Angel
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Publication number: 20240135234Abstract: A method for computing possibly optimal policies in reinforcement learning with multiple objectives and tradeoffs includes receiving a dataset comprising state, action, and reward information for objectives in a multiple objective environment. Tradeoff information indicating that a first vector comprising first values of the objectives in the multiple objective environment is preferred to a second vector comprising second values of the objectives in the multiple objective environment is received. A set of possibly optimal policies for the multiple objective environment is produced based on the dataset and the tradeoff information, where the set of possibly optimal policies indicates actions for an intelligent agent operating in the multiple objective environment to take.Type: ApplicationFiled: October 23, 2022Publication date: April 25, 2024Inventors: Radu Marinescu, Parikshit Ram, Djallel Bouneffouf, Tejaswini Pedapati, Paulito Palmes
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Publication number: 20230409957Abstract: According to one embodiment, a method, computer system, and computer program product for reinforcement learning is provided. The present invention may include training, using an offline dataset, a plurality of diverse reward models, and creating a policy based on an output of the reward models and a robustness operator of the reward models.Type: ApplicationFiled: June 17, 2022Publication date: December 21, 2023Inventors: Radu Marinescu, Parikshit Ram, Djallel BOUNEFFOUF, Tejaswini Pedapati, Paulito Palmes
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Publication number: 20230244752Abstract: An example system includes a processor to receive historical data, a formal quality measure, a quality threshold, and a mathematical optimization model. At least part of the mathematical optimization model is generated from the historical data. The processor can measure a quality of the mathematical optimization model using the formal quality measure. The processor can then augment the mathematical optimization model such that the measured quality of the augmented mathematical optimization model exceeds the target quality threshold.Type: ApplicationFiled: January 31, 2022Publication date: August 3, 2023Inventors: Eliezer Segev WASSERKRUG, Orit DAVIDOVICH, Evgeny SHINDIN, Dharmashankar SUBRAMANIAN, Parikshit RAM
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Methods for automatically configuring performance evaluation schemes for machine learning algorithms
Patent number: 11681931Abstract: 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.Type: GrantFiled: September 24, 2019Date of Patent: June 20, 2023Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Bo Zhang, Gregory Bramble, Parikshit Ram, Horst Cornelius Samulowitz -
Publication number: 20230186168Abstract: A computer-implemented method according to one embodiment includes issuing a hyperparameter optimization (HPO) query to a plurality of computing devices; receiving HPO results from each of the plurality of computing devices; generating a unified performance metric surface utilizing the HPO results from each of the plurality of computing devices; and determining optimal global hyperparameters, utilizing the unified performance metric surface.Type: ApplicationFiled: December 9, 2021Publication date: June 15, 2023Inventors: Yi Zhou, Parikshit Ram, Nathalie Baracaldo Angel, Theodoros Salonidis, Horst Cornelius Samulowitz, Martin Wistuba, Heiko H. Ludwig
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Publication number: 20230114013Abstract: Tradeoffs, objectives, and one or more machine learning models are analyzed. One or more instantiated machine learning pipelines are generated based on the tradeoffs and objectives. A first instantiated machine learning pipeline is preferred compared to a second instantiated machine learning pipeline based on the plurality of tradeoffs and objectives.Type: ApplicationFiled: October 12, 2021Publication date: April 13, 2023Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Radu MARINESCU, Parikshit RAM
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Patent number: 11625632Abstract: 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.Type: GrantFiled: April 17, 2020Date of Patent: April 11, 2023Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Akihiro Kishimoto, Djallel Bouneffouf, Bei Chen, Radu Marinescu, Parikshit Ram, Ambrish Rwat, Martin Wistuba
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Publication number: 20230098282Abstract: A plurality of objectives is received for a given dataset for an automated machine learning (autoML) process. A set of tradeoffs for the plurality of objectives are received that distribute weights to respective objectives. Pipelines are provided for the dataset that optimize each of the plurality of objectives according to the set of tradeoffs.Type: ApplicationFiled: September 30, 2021Publication date: March 30, 2023Inventors: Radu Marinescu, Parikshit Ram
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Publication number: 20230069913Abstract: Techniques for utilizing model and hyperparameter optimization for multi-objective machine learning are disclosed. In one example, a method comprises the following steps. One of a plurality of hyperparameter optimization operations and a plurality of model parameter optimization operations are performed to generate a first solution set. The other of the plurality of hyperparameter optimization operations and the plurality of model parameter optimization operations are performed to generate a second solution set. At least a portion of the first solution set and at least a portion of the second solution set are combined to generate a third solution set.Type: ApplicationFiled: September 9, 2021Publication date: March 9, 2023Inventors: Aswin Kannan, Vaibhav Saxena, Anamitra Roy Choudhury, Yogish Sabharwal, Parikshit Ram, Ashish Verma, Saurabh Manish Raje
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Patent number: 11599829Abstract: 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.Type: GrantFiled: April 29, 2020Date of Patent: March 7, 2023Assignee: International Business Machines CorporationInventors: Lior Horesh, Giacomo Nannicini, Oktay Gunluk, Sanjeeb Dash, Parikshit Ram, Alexander Gray
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Patent number: 11556816Abstract: 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.Type: GrantFiled: March 27, 2020Date of Patent: January 17, 2023Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Daniel Karl I. Weidele, Parikshit Ram, Dakuo Wang, Abel Nicolas Valente, Arunima Chaudhary
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Publication number: 20220188691Abstract: 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.Type: ApplicationFiled: December 11, 2020Publication date: June 16, 2022Inventors: Michael Katz, Parikshit Ram, Shirin Sohrabi Araghi, Octavian Udrea
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Publication number: 20220180146Abstract: 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.Type: ApplicationFiled: December 8, 2020Publication date: June 9, 2022Inventors: Vaibhav Saxena, Aswin Kannan, Saurabh Manish Raje, Parikshit Ram, Yogish Sabharwal, Ashish Verma
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Publication number: 20220076144Abstract: 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.Type: ApplicationFiled: September 9, 2020Publication date: March 10, 2022Inventors: Parikshit Ram, Dakuo Wang, Deepak Vijaykeerthy, Vaibhav Saxena, Sijia Liu, Arunima Chaudhary, Gregory Bramble, Horst Cornelius Samulowitz, Alexander Gray
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Publication number: 20210342732Abstract: 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.Type: ApplicationFiled: April 29, 2020Publication date: November 4, 2021Inventors: Lior Horesh, Giacomo Nannicini, Oktay Gunluk, Sanjeeb Dash, Parikshit Ram, Alexander Gray
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Publication number: 20210326736Abstract: 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.Type: ApplicationFiled: April 17, 2020Publication date: October 21, 2021Inventors: Akihiro Kishimoto, Djallel Boundeffouf, Bei Chen, Radu Marinescu, Parikshit Ram, Ambrish Rwat, Martin Wistuba
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Publication number: 20210304028Abstract: 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.Type: ApplicationFiled: March 27, 2020Publication date: September 30, 2021Inventors: Daniel Karl I. Weidele, Parikshit Ram, Dakuo Wang, Abel Nicolas Valente, Arunima Chaudhary
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Patent number: 11074728Abstract: 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.Type: GrantFiled: November 6, 2019Date of Patent: July 27, 2021Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Daniel Karl I. Weidele, Parikshit Ram