Patents by Inventor Jean-Baptiste Frederic George Tristan
Jean-Baptiste Frederic George Tristan 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).
-
Publication number: 20240168934Abstract: A first set and a second set are identified as operands for a set operation of a similarity analysis task iteration. Using respective minimum hash information arrays and contributor count arrays of the two sets, a minimum hash information array and contributor count array of a derived set resulting from the set operation is generated. An entry in the contributor count array of the derived set indicates the number of child sets of the derived set that meet a criterion with respect to a corresponding entry in the minimum hash information array of the derived set. The generated minimum hash information array and the contributor count array are stored as part of input for a subsequent iteration. After a termination criterion of the task is met, output of the task is stored.Type: ApplicationFiled: January 29, 2024Publication date: May 23, 2024Inventors: Michael Louis Wick, Jean-Baptiste Frederic George Tristan, Swetasudha Panda
-
Patent number: 11948102Abstract: A Bayesian test of demographic parity for learning to rank may be applied to determine ranking modifications. A fairness control system receiving a ranking of items may apply Bayes factors to determine a likelihood of bias for the ranking. These Bayes factors may include a factor for determining bias in each item and a factor for determining bias in the ranking of the items. An indicator of bias may be generated using the applied Bayes factors and the fairness control system may modify the ranking if the determines likelihood of bias satisfies modification criteria for the ranking.Type: GrantFiled: August 12, 2022Date of Patent: April 2, 2024Assignee: Oracle International CorporationInventors: Jean-Baptiste Frederic George Tristan, Michael Louis Wick, Swetasudha Panda
-
Patent number: 11921687Abstract: A first set and a second set are identified as operands for a set operation of a similarity analysis task iteration. Using respective minimum hash information arrays and contributor count arrays of the two sets, a minimum hash information array and contributor count array of a derived set resulting from the set operation is generated. An entry in the contributor count array of the derived set indicates the number of child sets of the derived set that meet a criterion with respect to a corresponding entry in the minimum hash information array of the derived set. The generated minimum hash information array and the contributor count array are stored as part of input for a subsequent iteration. After a termination criterion of the task is met, output of the task is stored.Type: GrantFiled: June 10, 2019Date of Patent: March 5, 2024Assignee: Oracle International CorporationInventors: Michael Louis Wick, Jean-Baptiste Frederic George Tristan, Swetasudha Panda
-
Publication number: 20230394371Abstract: Fairness of a trained classifier may be ensured by generating a data set for training, the data set generated using input data points of a feature space including multiple dimensions and according to different parameters including an amount of label bias, a control for discrepancy between rarity of features, and an amount of selection bias. Unlabeled data points of the input data comprising unobserved ground truths are labeled according to the amount of label bias and the input data sampled according to the amount of selection bias and the control for the discrepancy between the rarity of features. The classifier is then trained using the sampled and labeled data points as well as additional unlabeled data points. The trained classifier is then usable to determine unbiased classifications of one or more labels for one or more other data sets.Type: ApplicationFiled: August 22, 2023Publication date: December 7, 2023Inventors: Michael Louis Wick, Swetasudha Panda, Jean-Baptiste Frederic George Tristan
-
Patent number: 11775863Abstract: Fairness of a trained classifier may be ensured by generating a data set for training, the data set generated using input data points of a feature space including multiple dimensions and according to different parameters including an amount of label bias, a control for discrepancy between rarity of features, and an amount of selection bias. Unlabeled data points of the input data comprising unobserved ground truths are labeled according to the amount of label bias and the input data sampled according to the amount of selection bias and the control for the discrepancy between the rarity of features. The classifier is then trained using the sampled and labeled data points as well as additional unlabeled data points. The trained classifier is then usable to determine unbiased classifications of one or more labels for one or more other data sets.Type: GrantFiled: February 4, 2020Date of Patent: October 3, 2023Assignee: Oracle International CorporationInventors: Michael Louis Wick, Swetasudha Panda, Jean-Baptiste Frederic George Tristan
-
Publication number: 20220382768Abstract: A Bayesian test of demographic parity for learning to rank may be applied to determine ranking modifications. A fairness control system receiving a ranking of items may apply Bayes factors to determine a likelihood of bias for the ranking. These Bayes factors may include a factor for determining bias in each item and a factor for determining bias in the ranking of the items. An indicator of bias may be generated using the applied Bayes factors and the fairness control system may modify the ranking if the determines likelihood of bias satisfies modification criteria for the ranking.Type: ApplicationFiled: August 12, 2022Publication date: December 1, 2022Inventors: Jean-Baptiste Frederic George Tristan, Michael Louis Wick, Swetasudha Panda
-
Patent number: 11488579Abstract: A method of evaluating a language model using negative data may include accessing a first language model that is trained using a first training corpus, and accessing a second language model. The second language model may be configured to generate outputs that are less grammatical than outputs generated by the first language model. The method may also include training the second language model using a second training corpus, and generating output text from the second language model. The method may further include testing the first language model using the output text from the second language model.Type: GrantFiled: June 2, 2020Date of Patent: November 1, 2022Assignee: Oracle International CorporationInventors: Michael Louis Wick, Jean-Baptiste Frederic George Tristan, Jason Peck
-
Patent number: 11416500Abstract: A Bayesian test of demographic parity for learning to rank may be applied to determine ranking modifications. A fairness control system receiving a ranking of items may apply Bayes factors to determine a likelihood of bias for the ranking. These Bayes factors may include a factor for determining bias in each item and a factor for determining bias in the ranking of the items. An indicator of bias may be generated using the applied Bayes factors and the fairness control system may modify the ranking if the determines likelihood of bias satisfies modification criteria for the ranking.Type: GrantFiled: February 4, 2020Date of Patent: August 16, 2022Assignee: Oracle International CorporationInventors: Jean-Baptiste Frederic George Tristan, Michael Louis Wick, Swetasudha Panda
-
Publication number: 20220050848Abstract: Online post-processing may be performed for rankings generated with constrained utility maximization. A stream of data items may be received. A batch of data items from the stream may be ranked according to a ranking model trained to rank data items in a descending order of relevance. The batch of data items may be associated with a current time step. A re-ranking model may be applied to generate a re-ranking of the batch of data items according to a re-ranking policy that considers the current batch and previous batches with regard to a ranking constraint. The re-ranked items may then be sent to an application.Type: ApplicationFiled: July 6, 2021Publication date: February 17, 2022Inventors: Swetasudha Panda, Ariel Kobren, Jean-Baptiste Frederic George Tristan, Michael Louis Wick
-
Publication number: 20210375262Abstract: A method of evaluating a language model using negative data may include accessing a first language model that is trained using a first training corpus, and accessing a second language model. The second language model may be configured to generate outputs that are less grammatical than outputs generated by the first language model. The method may also include training the second language model using a second training corpus, and generating output text from the second language model. The method may further include testing the first language model using the output text from the second language model.Type: ApplicationFiled: June 2, 2020Publication date: December 2, 2021Applicant: Oracle International CorporationInventors: Michael Louis Wick, Jean-Baptiste Frederic George Tristan, Jason Peck
-
Publication number: 20210374361Abstract: A method for training a language model using negative data may include accessing a first training corpus comprising positive training data and accessing a second training corpus comprising negative training data. The method may further include training a first language model using at least the first training corpus, the second training corpus, and a maximum likelihood function. The maximum likelihood function may maximize the likelihood of the first language model predicting the positive training data while minimizing the likelihood of the first language model predicting the negative training data.Type: ApplicationFiled: June 2, 2020Publication date: December 2, 2021Applicant: Oracle International CorporationInventors: Michael Louis Wick, Jean-Baptiste Frederic George Tristan, Adam Craig Pocock, Katherine Silverstein
-
Publication number: 20210374582Abstract: A fairness metric of decisions pertaining to a plurality of candidates indicated in a data set is estimated. Using a Hamiltonian Monte Carlo sampling algorithm, sample sets corresponding to random variables of a null model and an alternate model are obtained. A respective kernel density estimator is fitted on at least some sample sets, and importance sampling is implemented on additional samples generated using the kernel density estimators. The estimated fairness metric is provided via one or more programmatic interfaces.Type: ApplicationFiled: June 26, 2020Publication date: December 2, 2021Inventors: Jean-Baptiste Frederic George Tristan, Michael Louis Wick, Stephen J. Green
-
Patent number: 11017151Abstract: A scalable hierarchical coreference method that employs a homomorphic compression scheme that supports addition and partial subtraction to more efficiently represent the data and the evolving intermediate results of probabilistic inference. The method may encode the features underlying conditional random field models of coreference resolution so that cosine similarities can be efficiently computed. The method may be applied to compressing features and intermediate inference results for conditional random fields. The method may allow compressed representations to be added and subtracted in a way that preserves the cosine similarities.Type: GrantFiled: March 27, 2020Date of Patent: May 25, 2021Assignee: Oracle International CorporationInventors: Michael Louis Wick, Jean-Baptiste Frederic George Tristan, Stephen Joseph Green
-
Patent number: 10990763Abstract: Systems and methods are disclosed to improve a topic modeling system that tunes a topic model for a set of topics from a corpus of documents, by allowing users to pre-inform the tuning process with bias parameters for desired associations in the topic model. In embodiments, the topic model may be a Latent Dirichlet Allocation (LDA) model. In embodiments, the bias parameter may indicate a fixed association where a particular word in a particular document is associated with a particular topic. In embodiments, the bias parameter may specify a weight value that biases the inference process with regard to a particular association. Advantageously, the disclosed features allow users to specify a small number of parameters to steer the tuning process towards a set of desired topics. As a result, the topic model may be generated more quickly and with more useful topics.Type: GrantFiled: May 9, 2019Date of Patent: April 27, 2021Assignee: Oracle International CorporationInventors: Daniel Peterson, Jean-Baptiste Frederic George Tristan, Robert James Oberbreckling
-
Publication number: 20200387743Abstract: A first set and a second set are identified as operands for a set operation of a similarity analysis task iteration. Using respective minimum hash information arrays and contributor count arrays of the two sets, a minimum hash information array and contributor count array of a derived set resulting from the set operation is generated. An entry in the contributor count array of the derived set indicates the number of child sets of the derived set that meet a criterion with respect to a corresponding entry in the minimum hash information array of the derived set. The generated minimum hash information array and the contributor count array are stored as part of input for a subsequent iteration. After a termination criterion of the task is met, output of the task is stored.Type: ApplicationFiled: June 10, 2019Publication date: December 10, 2020Inventors: Michael Louis Wick, Jean-Baptiste Frederic George Tristan, Swetasudha Panda
-
Publication number: 20200372035Abstract: A Bayesian test of demographic parity for learning to rank may be applied to determine ranking modifications. A fairness control system receiving a ranking of items may apply Bayes factors to determine a likelihood of bias for the ranking. These Bayes factors may include a factor for determining bias in each item and a factor for determining bias in the ranking of the items. An indicator of bias may be generated using the applied Bayes factors and the fairness control system may modify the ranking if the determines likelihood of bias satisfies modification criteria for the ranking.Type: ApplicationFiled: February 4, 2020Publication date: November 26, 2020Inventors: Jean-Baptiste Frederic George Tristan, Michael Louis Wick, Swetasudha Panda
-
Publication number: 20200372406Abstract: Fairness of a trained classifier may be ensured by generating a data set for training, the data set generated using input data points of a feature space including multiple dimensions and according to different parameters including an amount of label bias, a control for discrepancy between rarity of features, and an amount of selection bias. Unlabeled data points of the input data comprising unobserved ground truths are labeled according to the amount of label bias and the input data sampled according to the amount of selection bias and the control for the discrepancy between the rarity of features. The classifier is then trained using the sampled and labeled data points as well as additional unlabeled data points. The trained classifier is then usable to determine unbiased classifications of one or more labels for one or more other data sets.Type: ApplicationFiled: February 4, 2020Publication date: November 26, 2020Inventors: Michael Louis Wick, Swetasudha Panda, Jean-Baptiste Frederic George Tristan
-
Publication number: 20200372290Abstract: A Bayesian test of demographic parity for learning to rank may be applied to determine ranking modifications. A fairness control system receiving a ranking of items may apply Bayes factors to determine a likelihood of bias for the ranking. These Bayes factors may include a factor for determining bias in each item and a factor for determining bias in the ranking of the items. An indicator of bias may be generated using the applied Bayes factors and the fairness control system may modify the ranking if the determines likelihood of bias satisfies modification criteria for the ranking.Type: ApplicationFiled: February 4, 2020Publication date: November 26, 2020Inventors: Jean-Baptiste Frederic George Tristan, Pallika Haridas Kanani, Michael Louis Wick, Swetasudha Panda, Haniyeh Mahmoudian
-
Publication number: 20200279019Abstract: Systems and methods are disclosed to improve a topic modeling system that tunes a topic model for a set of topics from a corpus of documents, by allowing users to pre-inform the tuning process with bias parameters for desired associations in the topic model. In embodiments, the topic model may be a Latent Dirichlet Allocation (LDA) model. In embodiments, the bias parameter may indicate a fixed association where a particular word in a particular document is associated with a particular topic. In embodiments, the bias parameter may specify a weight value that biases the inference process with regard to a particular association. Advantageously, the disclosed features allow users to specify a small number of parameters to steer the tuning process towards a set of desired topics. As a result, the topic model may be generated more quickly and with more useful topics.Type: ApplicationFiled: May 9, 2019Publication date: September 3, 2020Inventors: Daniel Peterson, Jean-Baptiste Frederic George Tristan, Robert James Oberbreckling
-
Publication number: 20200226318Abstract: A scalable hierarchical coreference method that employs a homomorphic compression scheme that supports addition and partial subtraction to more efficiently represent the data and the evolving intermediate results of probabilistic inference. The method may encode the features underlying conditional random field models of coreference resolution so that cosine similarities can be efficiently computed. The method may be applied to compressing features and intermediate inference results for conditional random fields. The method may allow compressed representations to be added and subtracted in a way that preserves the cosine similarities.Type: ApplicationFiled: March 27, 2020Publication date: July 16, 2020Inventors: Michael Louis Wick, Jean-Baptiste Frederic George Tristan, Stephen Joseph Green