Patents by Inventor Swetasudha Panda
Swetasudha Panda 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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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
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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
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Publication number: 20230409969Abstract: Bias in a language model generated through fine tuning of a pre-trained language model may be mitigated, whether the bias may be incorporated in the pre-trained language model or in fine-tuning data. A pre-trained language model may be fine-tuned using downstream training data. Prior to tuning, elements within the downstream data may be identified that either match or serve as proxies for one or more identity elements associated with training bias sensitivity. Proxy elements may be identified using an analysis of distributions of the downstream elements and distributions of identity elements. Once the elements are identified, instances of the identified elements may be replaced in the downstream data with one or more masking element to generate masked downstream data. A fine-tuned language model with reduced bias may then be generated from the pre-trained language model by tuning the pre-trained language model using the masked downstream data.Type: ApplicationFiled: February 28, 2023Publication date: December 21, 2023Inventors: Swetasudha Panda, Ariel Kobren, Michael Louis Wick, Qinlan Shen
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Publication number: 20230401285Abstract: Techniques are disclosed for augmenting data sets used for training machine learning models and for generating predictions by trained machine learning models. The techniques generate synthesized data from sample data and train a machine learning model using the synthesized data to augment a sample data set. Embodiments selectively partition the sample data set and synthesized data into a training data and a validation data, which are used to generate and select machine learning models.Type: ApplicationFiled: September 6, 2022Publication date: December 14, 2023Applicant: Oracle International CorporationInventors: Ariel Gedaliah Kobren, Swetasudha Panda, Michael Louis Wick, Qinlan Shen, Jason Anthony Peck
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Publication number: 20230401286Abstract: Techniques are disclosed for augmenting data sets used for training machine learning models and for generating predictions by trained machine learning models. These techniques may increase a number and diversity of examples within an initial training dataset of sentences by extracting a subset of words from the existing training dataset of sentences. The techniques may conserve scarce sample data in few-shot situations by training a data generation model using general data obtained from a general data source.Type: ApplicationFiled: September 6, 2022Publication date: December 14, 2023Applicant: Oracle International CorporationInventors: Ariel Gedaliah Kobren, Swetasudha Panda, Michael Louis Wick, Qinlan Shen, Jason Anthony Peck
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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
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Publication number: 20230368015Abstract: Techniques are described herein for training and applying machine learning models. The techniques include implementing an entropy-based loss function for training high-capacity machine learning models, such as deep neural networks, with anti-modeling. The entropy-based loss function may cause the model to have high entropy on negative data, helping prevent the model from becoming confidently wrong about the negative data while reducing the likelihood of generalizing from disfavored signals.Type: ApplicationFiled: September 8, 2022Publication date: November 16, 2023Applicant: Oracle International CorporationInventors: Michael Louis Wick, Ariel Gedaliah Kobren, Swetasudha Panda
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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
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Publication number: 20230047092Abstract: User-level privacy preservation is implemented within federated machine learning. An aggregation server may distribute a machine learning model to multiple users each including respective private datasets. Individual users may train the model using the local, private dataset to generate one or more parameter updates. Prior to sending the generated parameter updates to the aggregation server for incorporation into the machine learning model, a user may modify the parameter updates by applying respective noise values to individual ones of the parameter updates to ensure differential privacy for the dataset private to the user. The aggregation server may then receive the respective modified parameter updates from the multiple users and aggregate the updates into a single set of parameter updates to update the machine learning model. The federated machine learning may further include iteratively performing said sending, training, modifying, receiving, aggregating and updating steps.Type: ApplicationFiled: May 11, 2022Publication date: February 16, 2023Inventors: Virendra Marathe, Pallika Haridas Kanani, Daniel Peterson, Swetasudha Panda
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Publication number: 20230032208Abstract: Techniques are disclosed for augmenting data sets used for training machine learning models and for generating predictions by trained machine learning models. These techniques may increase a number (and diversity) of examples within an initial training dataset of sentences by extracting a subset of words from the existing training dataset of sentences. The extracted subset includes no stopwords and fewer content words than found in the initial training dataset. The remaining words may be re-ordered. Using the extracted and re-ordered subset of words, the dataset generation model produces a second set of sentences that are different from the first set. The second set of sentences may be used to increase a number of examples in classes with few examples.Type: ApplicationFiled: July 30, 2021Publication date: February 2, 2023Applicant: Oracle International CorporationInventors: Ariel Gedaliah Kobren, Naveen Jafer Nizar, Michael Louis Wick, Swetasudha Panda
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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
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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
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Publication number: 20220245339Abstract: Debiasing pre-trained sentence encoders with probabilistic dropouts may be performed by various systems, services, or applications. A sentence may be received, where the words of the sentence may be provided as tokens to an encoder of a machine learning model. A token-wise correlation using semantic orientation may be determined to determine a bias score for the tokens in the input sentence. A probability of dropout that for tokens in the input sentence may be determined from the bias scores. The machine learning model may be trained or tuned based on the probabilities of dropout for the tokens in the input sentence.Type: ApplicationFiled: January 31, 2022Publication date: August 4, 2022Inventors: Swetasudha Panda, Ariel Kobren, Michael Louis Wick, Stephen Green
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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
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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
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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
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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
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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