Patents by Inventor Martin WISTUBA
Martin WISTUBA 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: 20240112011Abstract: A system and method for continual learning in a provider network. The method is configured to implement or interface with a system which implements a semi-automated or fully automated architecture of continual machine learning, the semi-automated or fully automated architecture implementing user-configurable model retraining or hyperparameter tuning, which is enabled by a provider network. This functions to adapt a model over time to new information in the training data while also providing a user-friendly, flexible, and customizable continual learning process.Type: ApplicationFiled: September 30, 2022Publication date: April 4, 2024Inventors: Giovanni ZAPPELLA, Lukas Stefan BALLES, Beyza ERMIS, Martin WISTUBA, Cedric Philippe ARCHAMBEAU
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Patent number: 11847546Abstract: Embodiments for automatic data preprocessing for a machine learning operation by a processor. For each data instance in a set of data instances, a sequence of actions may be automatically learned in a reinforcement learning environment to be applied for preprocessing each data instance separately. Each of the data instances may be preprocessed for use by one or more machine learning models according to the learned sequence of actions.Type: GrantFiled: May 17, 2018Date of Patent: December 19, 2023Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Ngoc Minh Tran, Mathieu Sinn, Thanh Lam Hoang, Martin Wistuba
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Patent number: 11681796Abstract: Various embodiments are provided for securing machine learning models by one or more processors in a computing system. One or more hardened machine learning models that are secured against adversarial attacks are provided by applying one or more of a plurality of combinations of selected preprocessing operations from one or more machine learning models, a data set used for hardening the one or more machine learning models, a list of preprocessors, and a selected number of learners.Type: GrantFiled: September 10, 2019Date of Patent: June 20, 2023Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Ngoc Minh Tran, Mathieu Sinn, Maria-Irina Nicolae, Martin Wistuba, Ambrish Rawat, Beat Buesser
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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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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: 20230088588Abstract: Embodiments are disclosed for a method. The method includes validating training data that is provided for training a machine learning model using ordinary differential equations. The method further includes generating pre-processed training data from the validated training data by generating encrypted training data from the validated training data using homomorphic encryption and generating random noise based on the validated training data. The method also includes training the machine learning model adversarially with the pre-processed training data.Type: ApplicationFiled: September 23, 2021Publication date: March 23, 2023Inventors: Mansura Habiba, GOKHAN SAGIRLAR, Martin Wistuba
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Patent number: 11568249Abstract: Various embodiments are provided for automating decision making for a neural architecture search by one or more processors in a computing system. One or more specifications may be automatically selected for a dataset, tasks, and one or more constraints for a neural architecture search. The neural architecture search may be performed based on the one or more specifications. A deep learning model may be suggested, predicted, and/or configured for the dataset, the tasks, and the one or more constraints based on the neural architecture search.Type: GrantFiled: April 7, 2020Date of Patent: January 31, 2023Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Ambrish Rawat, Martin Wistuba, Beat Buesser, Mathieu Sinn, Sharon Qian, Suwen Lin
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Publication number: 20230004843Abstract: A computer-implemented method for automated policy decision making optimization is disclosed. The computer-implemented method includes creating a dataset from a tabular database, wherein the dataset includes one or more columns selected as state variables, a column selected as action variables, and a column selected as reward variables. The computer-implemented method further includes determining a candidate function approximator Q based on applying at least one state variable, one action variable, and one reward variable to a trained regression model. The computer-implemented method further includes learning a decision policy based on applying the candidate function approximator Q to a reinforcement learning algorithm. The computer-implemented method further includes determining, based on the learned decision policy, an expected reward.Type: ApplicationFiled: June 30, 2021Publication date: January 5, 2023Inventors: Radu Marinescu, Akihiro Kishimoto, Paulito Palmes, Martin Wistuba
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Publication number: 20220198222Abstract: A computer receives a dataset and a set of ML pipeline components to generate a preferred ensemble of Machine Learning (ML) pipelines. An Automated Learning (AutoML) tool is applied to generate a plurality of ML pipelines. A performance value is determined for each pipeline, and a set of candidate pipelines is identified based on the performance values. The candidate pipelines are combined into candidate ensembles. A database provides historic performance data for a plurality of historic ensembles applied to a plurality of historic datasets. A metamodel is trained to identify patterns within the historic performance data, and a applies the patterns to generate predicted ensemble performance values for the candidate ensembles. A preferred ensemble is selected based on the predicted performance value rankings.Type: ApplicationFiled: December 17, 2020Publication date: June 23, 2022Inventors: Ambrish Rawat, Martin Wistuba
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Publication number: 20220172038Abstract: A system and method for automatically generating deep neural network architectures for time series prediction. The system includes a processor for: receiving a prediction context associated with a current use case; based on the associated prediction context, selecting a prediction model network configured for a current use case time series prediction task; replicating the selected prediction model network to create a plurality of candidate prediction model networks; inputting a time series data to each of the plurality of the candidate prediction model network; train, in parallel, each respective candidate prediction model network of the plurality with the input time series data; modifying each of the plurality of the candidate prediction model network by applying a respective different set of one or more model parameters while being trained in parallel; and determine a fittest modified prediction model network for solving the current use case time series prediction task.Type: ApplicationFiled: November 30, 2020Publication date: June 2, 2022Inventors: Bei Chen, Dakuo Wang, Martin Wistuba, Beat Buesser, Long VU, Chuang Gan, Mathieu Sinn
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Patent number: 11334671Abstract: One or more hardened machine learning models are secured against adversarial attacks by adding adversarial protection to one or more previously trained machine learning models. To generate the hardened machine learning models, the previously trained machine learning models are retrained and extended using preprocessing layers or using additional network layers which test model performance on benign or adversarial samples. A rollback strategy is additionally implemented to retain intermediate model states during the retraining to provide recovery if a training collapse is detected.Type: GrantFiled: October 14, 2019Date of Patent: May 17, 2022Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Beat Buesser, Maria-Irina Nicolae, Ambrish Rawat, Mathieu Sinn, Ngoc Minh Tran, Martin Wistuba
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Patent number: 11288408Abstract: Embodiments for providing adversarial protection to computing display devices by a processor. Security defenses may be provided on one or more image display devices against automated media analysis by using adversarial noise, an adversarial patch, or a combination thereof.Type: GrantFiled: October 14, 2019Date of Patent: March 29, 2022Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Beat Buesser, Maria-Irina Nicolae, Ambrish Rawat, Mathieu Sinn, Ngoc Minh Tran, Martin Wistuba
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Publication number: 20220092464Abstract: Various embodiments are provided for accelerating machine learning in a computing environment by one or more processors in a computing system. Selected data may be received for training machine learning pipelines. Each of the machine learning pipelines may be scored according to one or more learning curves while training on selected data. Completion of the training on the selected data may be permitted for those of the machine learning pipelines having a score greater than a selected threshold. The training on the selected data may be terminated, prior to completion, on those of the machine learning pipelines having a score less than a selected threshold.Type: ApplicationFiled: September 23, 2020Publication date: March 24, 2022Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Martin WISTUBA, Tejaswini PEDAPATI
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Publication number: 20220051049Abstract: A computer automatically selects a machine learning model pipeline using a meta-learning machine learning model. The computer receives ground truth data and pipeline preference metadata. The computer determines a group of pipelines appropriate for the ground truth data, and each of the pipelines includes an algorithm. The pipelines may include data preprocessing routines. The computer generates hyperparameter sets for the pipelines. The computer applies preprocessing routines to ground truth data to generate a group of preprocessed sets of said ground truth data and ranks hyperparameter set performance for each pipeline to establish a preferred set of hyperparameters for each of pipeline. The computer selects favored data features and applies each of the pipelines, with associated sets of preferred hyperparameters, to score the favored data features of the preprocessed ground truth data. The computer ranks pipeline performance and selects a candidate pipeline according to the ranking.Type: ApplicationFiled: August 11, 2020Publication date: February 17, 2022Inventors: Dakuo Wang, Chuang Gan, Gregory Bramble, Lisa Amini, Horst Cornelius Samulowitz, Kiran A. Kate, Bei Chen, Martin Wistuba, Alexandre Evfimievski, Ioannis Katsis, Yunyao Li, Adelmo Cristiano Innocenza Malossi, Andrea Bartezzaghi, Ban Kawas, Sairam Gurajada, Lucian Popa, Tejaswini Pedapati, 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: 20210312276Abstract: Various embodiments are provided for automating decision making for a neural architecture search by one or more processors in a computing system. One or more specifications may be automatically selected for a dataset, tasks, and one or more constraints for a neural architecture search. The neural architecture search may be performed based on the one or more specifications. A deep learning model may be suggested, predicted, and/or configured for the dataset, the tasks, and the one or more constraints based on the neural architecture search.Type: ApplicationFiled: April 7, 2020Publication date: October 7, 2021Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Ambrish RAWAT, Martin WISTUBA, Beat BUESSER, Mathieu SINN, Sharon QIAN, Suwen LIN
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Patent number: 11036857Abstract: A method for protecting a machine learning model includes: generating a first adversarial example by modifying an original input using an attack tactic, wherein the model accurately classifies the original input but does not accurately classify at least the first adversarial example; training a defender to protect the model from the first adversarial example by updating a strategy of the defender based on predictive results from classifying the first adversarial example; updating the attack tactic based on the predictive results from classifying the first adversarial example; generating a second adversarial example by modifying the original input using the updated attack tactic, wherein the trained defender does not protect the model from the second adversarial example; and training the defender to protect the model from the second adversarial example by updating the at least one strategy of the defender based on results obtained from classifying the second adversarial example.Type: GrantFiled: November 15, 2018Date of Patent: June 15, 2021Assignee: International Business Machines CorporationInventors: Ngoc Minh Tran, Mathieu Sinn, Ambrish Rawat, Maria-Irina Nicolae, Martin Wistuba
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Publication number: 20210110071Abstract: Embodiments for providing adversarial protection to computing display devices by a processor. Security defenses may be provided on one or more image display devices against automated media analysis by using adversarial noise, an adversarial patch, or a combination thereof.Type: ApplicationFiled: October 14, 2019Publication date: April 15, 2021Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Beat BUESSER, Maria-Irina NICOLAE, Ambrish RAWAT, Mathieu SINN, Ngoc Minh TRAN, Martin WISTUBA
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Publication number: 20210110045Abstract: Various embodiments are provided for securing trained machine learning models by one or more processors in a computing system. One or more hardened machine learning models are secured against adversarial attacks by adding adversarial protection to one or more trained machine learning model.Type: ApplicationFiled: October 14, 2019Publication date: April 15, 2021Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Beat BUESSER, Maria-Irina NICOLAE, Ambrish RAWAT, Mathieu SINN, Ngoc Minh TRAN, Martin WISTUBA
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Publication number: 20210073376Abstract: Various embodiments are provided for securing machine learning models by one or more processors in a computing system. One or more hardened machine learning models that are secured against adversarial attacks are provided by applying one or more of a plurality of combinations of selected preprocessing operations from one or more machine learning models, a data set used for hardening the one or more machine learning models, a list of preprocessors, and a selected number of learners.Type: ApplicationFiled: September 10, 2019Publication date: March 11, 2021Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Ngoc Minh TRAN, Mathieu SINN, Maria-Irina NICOLAE, Martin WISTUBA, Ambrish RAWAT, Beat BUESSER