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11.发明授权公开(公告)号：US12299553B2
公开(公告)日：2025-05-13
申请号：US18075824
申请日：2022-12-06
Applicant: Oracle International Corporation
Inventor： Yasha Pushak , Constantin Le Clei , Fatjon Zogaj , Hesam Fathi Moghadam , Sungpack Hong , Hassan Chafi
Abstract: In a computer, each of multiple anomaly detectors infers an anomaly score for each of many tuples. For each tuple, a synthetic label is generated that indicates for each anomaly detector: the anomaly detector, the anomaly score inferred by the anomaly detector for the tuple and, for each of multiple contamination factors, the contamination factor and, based on the contamination factor, a binary class of the anomaly score. For each particular anomaly detector excluding a best anomaly detector, a similarity score is measured for each contamination factor. The similarity score indicates how similar, between the particular anomaly detector and the best anomaly detector, are the binary classes of labels with that contamination factor. For each contamination factor, a combined similarity score is calculated based on the similarity scores for the contamination factor. Based on a contamination factor that has the highest combined similarity score, the computer detects that an additional anomaly detector is inaccurate.
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公开(公告)号：US20250077876A1
公开(公告)日：2025-03-06
申请号：US18239416
申请日：2023-08-29
Applicant: Oracle International Corporation
Inventor： Moein Owhadi Kareshk , Giulia Carocari , Yasha Pushak , Hesam Fathi Moghadam , Sungpack Hong , Hassan Chafi
IPC: G06N3/09
Abstract: Techniques for selecting machine-learned (ML) models using diversity criteria are provided. In one technique, for each ML model of multiple ML models, output data is generated based on input data to the ML model. Multiple pairs of ML models are identified, where each ML model in the multiple pairs is from the multiple ML models. For each pair of ML models in the multiple pairs of ML models: (1) first output data that was previously generated by a first ML model in the pair is identified; (2) second output data that was previously generated by a second ML model in the pair is identified; (3) a diversity value that is based on the first and second output data is generated; and (4) the diversity value is added to a set of diversity values. A subset of the multiple ML models is selected based on the set of diversity values.
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13.发明公开公开(公告)号：US20240095231A1
公开(公告)日：2024-03-21
申请号：US18075824
申请日：2022-12-06
Applicant: Oracle International Corporation
Inventor： Yasha Pushak , Constantin Le Clei , Fatjon Zogaj , Hesam Fathi Moghadam , Sungpack Hong , Hassan Chafi
IPC: G06F16/23
CPC classification number: G06F16/2365
Abstract: In a computer, each of multiple anomaly detectors infers an anomaly score for each of many tuples. For each tuple, a synthetic label is generated that indicates for each anomaly detector: the anomaly detector, the anomaly score inferred by the anomaly detector for the tuple and, for each of multiple contamination factors, the contamination factor and, based on the contamination factor, a binary class of the anomaly score. For each particular anomaly detector excluding a best anomaly detector, a similarity score is measured for each contamination factor. The similarity score indicates how similar, between the particular anomaly detector and the best anomaly detector, are the binary classes of labels with that contamination factor. For each contamination factor, a combined similarity score is calculated based on the similarity scores for the contamination factor. Based on a contamination factor that has the highest combined similarity score, the computer detects that an additional anomaly detector is inaccurate.
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公开(公告)号：US20230334364A1
公开(公告)日：2023-10-19
申请号：US18075667
申请日：2022-12-06
Applicant: Oracle International Corporation
Inventor： Yasha Pushak , Robert Wayne Harlow , Constantin Le Clei , Hesam Fathi Moghadam , Sungpack Hong , Hassan Chafi
IPC: G06N20/00
CPC classification number: G06N20/00
Abstract: In an embodiment in a computer, each of several anomaly detectors infers a respective anomaly inference for each of many test tuples. For each available anomaly detector that is not the candidate anomaly detector, a respective fitness score is measured for the candidate anomaly detector that indicates how similar are anomaly inferences of the candidate anomaly detector to anomaly inferences of the available anomaly detector. Fitness scores of the candidate anomaly detector are combined into a combined fitness score for the candidate anomaly detector. The best anomaly detector that has a highest combined fitness score is selected for further operation such as inferring an anomaly inference for a new tuple while retraining or in production.
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公开(公告)号：US11720751B2
公开(公告)日：2023-08-08
申请号：US17146375
申请日：2021-01-11
Applicant: Oracle International Corporation
Inventor： Zahra Zohrevand , Tayler Hetherington , Karoon Rashedi Nia , Yasha Pushak , Sanjay Jinturkar , Nipun Agarwal
IPC: G06F17/00 , G06F40/284 , G06F40/30 , G06F40/166 , G06N20/00
CPC classification number: G06F40/284 , G06F40/166 , G06F40/30 , G06N20/00
Abstract: A model-agnostic global explainer for textual data processing (NLP) machine learning (ML) models, “NLP-MLX”, is described herein. NLP-MLX explains global behavior of arbitrary NLP ML models by identifying globally-important tokens within a textual dataset containing text data. NLP-MLX accommodates any arbitrary combination of training dataset pre-processing operations used by the NLP ML model. NLP-MLX includes four main stages. A Text Analysis stage converts text in documents of a target dataset into tokens. A Token Extraction stage uses pre-processing techniques to efficiently pre-filter the complete list of tokens into a smaller set of candidate important tokens. A Perturbation Generation stage perturbs tokens within documents of the dataset to help evaluate the effect of different tokens, and combinations of tokens, on the model's predictions. Finally, a Token Evaluation stage uses the ML model and perturbed documents to evaluate the impact of each candidate token relative to predictions for the original documents.
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Patent Agency Ranking
公开(公告)号：US20220188645A1
公开(公告)日：2022-06-16
申请号：US17124018
申请日：2020-12-16
Applicant: Oracle International Corporation
Inventor： Karoon Rashedi Nia , Tayler Hetherington , Zahra Zohrevand , Yasha Pushak , Sanjay Jinturkar , Nipun Agarwal
Abstract: Herein are counterfactual explanations of machine learning (ML) inferencing provided by generative adversarial networks (GANs) that ensure realistic counterfactuals and use latent spaces to optimize perturbations. In an embodiment, a first computer trains a generator model in a GAN. A same or second computer hosts a classifier model that inferences an original label for original feature values respectively for many features. Runtime ML explainability (MLX) occurs on the first or second or a third computer as follows. The generator model from the GAN generates a sequence of revised feature values that are based on noise. The noise is iteratively optimized based on a distance between the original feature values and current revised feature values in the sequence of revised feature values. The classifier model inferences a current label respectively for each counterfactual in the sequence of revised feature values. Satisfactory discovered counterfactuals are promoted as explanations of behavior of the classifier model.
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公开(公告)号：US20240403674A1
公开(公告)日：2024-12-05
申请号：US18529300
申请日：2023-12-05
Applicant: Oracle International Corporation
Inventor： Mathieu Godbout , Yasha Pushak , Hesam Fathi Moghadam , Sungpack Hong , Hassan Chafi
Abstract: In an embodiment, a computer infers, from an input (e.g. that represents a person) that contains a value of a sensitive feature that has a plurality of multipliers, a probability of a majority class (i.e. an outcome). Based on the value of the sensitive feature in the input, from the multipliers of the sensitive feature, a multiplier is selected that is specific to both of the sensitive feature and the value of the sensitive feature. The input is classified based on a multiplicative product of the probability of the majority class and the multiplier that is specific to both of the sensitive feature and the value of the sensitive feature. In an embodiment, a black-box bi-objective optimizer generates multipliers on a Pareto frontier from which a user may interactively select a combination of multipliers that provide a best tradeoff between fairness and accuracy.
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公开(公告)号：US20240303541A1
公开(公告)日：2024-09-12
申请号：US18386196
申请日：2023-11-01
Applicant: Oracle International Corporation
Inventor： Yasha Pushak , Ali Seyfi , Hesam Fathi Moghadam , Sungpack Hong , Hassan Chafi
Abstract: In an embodiment, a computer generates, from an input, an inference that contains multiple probabilities respectively for multiple mutually exclusive classes that contain a first class and a second class. The probabilities contain (e.g. due to overfitting) a higher probability for the first class that is higher than a lower probability for the second class. In response to a threshold exceeding the higher probability, the input is automatically and more accurately classified as the second class. One, some, or almost all classes may have a respective distinct threshold that can be concurrently applied for acceleration. Data parallelism may simultaneously apply a threshold to a batch of multiple inputs for acceleration.
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公开(公告)号：US20240303515A1
公开(公告)日：2024-09-12
申请号：US18512438
申请日：2023-11-17
Applicant: Oracle International Corporation
Inventor： Zahra Zohrevand , Ehsan Soltan Aghai , Yasha Pushak , Hesam Fathi Moghadam , Sungpack Hong , Hassan Chafi
IPC: G06N5/04
CPC classification number: G06N5/04
Abstract: A computer stores a reference corpus that consists of many reference points that each has a respective class. Later, an expected class and a subject point (i.e. instance to explain) that does not have the expected class are received. Multiple reference points that have the expected class are selected as starting points. Based on the subject point and the starting points, multiple discrete interpolated points are generated that have the expected class. Based on the subject point and the discrete interpolated points, multiple continuous interpolated points are generated that have the expected class. A counterfactual explanation of why the subject point does not have the expected class is directly generated based on continuous interpolated point(s) and, thus, indirectly generated based on the discrete interpolated points. For acceleration, neither way of interpolation (i.e. counterfactual generation) is iterative. Generated interpolated points can be reused to amortize resources consumed while generating counterfactuals.
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20.发明授权公开(公告)号：US11966275B2
公开(公告)日：2024-04-23
申请号：US17992743
申请日：2022-11-22
Applicant: Oracle International Corporation
Inventor： Ali Seyfi , Yasha Pushak , Hesam Fathi Moghadam , Sungpack Hong , Hassan Chafi
CPC classification number: G06F11/006 , G06N20/00 , G06F2201/82
Abstract: The present invention relates to machine learning (ML) explainability (MLX). Herein are local explanation techniques for black box ML models based on coalitions of features in a dataset. In an embodiment, a computer receives a request to generate a local explanation of which coalitions of features caused an anomaly detector to detect an anomaly. During unsupervised generation of a new coalition, a first feature is randomly selected from features in a dataset. Which additional features in the dataset can join the coalition, because they have mutual information with the first feature that exceeds a threshold, is detected. For each feature that is not in the coalition, values of the feature are permuted in imperfect copies of original tuples in the dataset. An average anomaly score of the imperfect copies is measured. Based on the average anomaly score of the imperfect copies, a local explanation is generated that references (e.g. defines) the coalition.
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