公开(公告)号：US20250094862A1

公开(公告)日：2025-03-20

申请号：US18529182

申请日：2023-12-05

Applicant: Oracle International Corporation

Inventor： Yasha Pushak ,  Mathieu Godbout ,  Hesam Fathi Moghadam ,  Sungpack Hong ,  Hassan Chafi

IPC: G06N20/00

Abstract: In an embodiment, a computer generates a respective original inference from each of many records. Permuted values are selected for a feature from original values of the feature. Based on the permuted values for the feature, a permuted inference is generated from each record. Fairness and accuracy of the original and permuted inferences are measured. For each of many features, the computer measures a respective impact on fairness of a machine learning model, and a respective impact on accuracy of the machine learning model. A global explanation of the machine learning model is generated and presented based on, for multiple features, the impacts on fairness and accuracy. Based on the global explanation, an interactive indication to exclude or include a particular feature is received. The machine learning model is (re-)trained based on the interactive indication to exclude or include the particular feature, which may increase the fairness of the model.

公开(公告)号：US11687540B2

公开(公告)日：2023-06-27

申请号：US17179265

申请日：2021-02-18

Applicant: Oracle International Corporation

Inventor： Yasha Pushak ,  Tayler Hetherington ,  Karoon Rashedi Nia ,  Zahra Zohrevand ,  Sanjay Jinturkar ,  Nipun Agarwal

IPC: G06F16/2458 ,  G06N20/00

CPC classification number: G06F16/2465 ,  G06N20/00

Abstract: Techniques are described for fast approximate conditional sampling by randomly sampling a dataset and then performing a nearest neighbor search on the pre-sampled dataset to reduce the data over which the nearest neighbor search must be performed and, according to an embodiment, to effectively reduce the number of nearest neighbors that are to be found within the random sample. Furthermore, KD-Tree-based stratified sampling is used to generate a representative sample of a dataset. KD-Tree-based stratified sampling may be used to identify the random sample for fast approximate conditional sampling, which reduces variance in the resulting data sample. As such, using KD-Tree-based stratified sampling to generate the random sample for fast approximate conditional sampling ensures that any nearest neighbor selected, for a target data instance, from the random sample is likely to be among the nearest neighbors of the target data instance within the unsampled dataset.

公开(公告)号：US20220229983A1

公开(公告)日：2022-07-21

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: G06F40/284 ,  G06N20/00 ,  G06F40/166 ,  G06F40/30

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.

公开(公告)号：US20240095604A1

公开(公告)日：2024-03-21

申请号：US18075784

申请日：2022-12-06

Applicant: Oracle International Corporation

Inventor： Fatjon Zogaj ,  Yasha Pushak ,  Hesam Fathi Moghadam ,  Sungpack Hong ,  Hassan Chafi

IPC: G06N20/20

CPC classification number: G06N20/20

Abstract: A computer sorts empirical validation scores of validated training scenarios of an anomaly detector. Each training scenario has a dataset to train an instance of the anomaly detector that is configured with values for hyperparameters. Each dataset has values for metafeatures. For each predefined ranking percentage, a subset of best training scenarios is selected that consists of the ranking percentage of validated training scenarios having the highest empirical validation scores. Linear optimizers train to infer a value for a hyperparameter. Into many distinct unvalidated training scenarios, a scenario is generated that has metafeatures values and hyperparameters values that contains the value inferred for that hyperparameter by a linear optimizer. For each unvalidated training scenario, a validation score is inferred. A best linear optimizer is selected having a highest combined inferred validation score. For a new dataset, the best linear optimizer infers a value of that hyperparameter.

公开(公告)号：US20240070471A1

公开(公告)日：2024-02-29

申请号：US17900779

申请日：2022-08-31

Applicant: Oracle International Corporation

Inventor： Yasha Pushak ,  Moein Owhadi Kareshk ,  Hesam Fathi Moghadam ,  Sungpack Hong ,  Hassan Chafi

IPC: G06N3/12

CPC classification number: G06N3/126

Abstract: Principal component analysis (PCA) accelerates and increases accuracy of genetic algorithms. In an embodiment, a computer generates many original chromosomes. Each original chromosome contains a sequence of original values. Each position in the sequences in the original chromosomes corresponds to only one respective distinct parameter in a set of parameters to be optimized. Based on the original chromosomes, many virtual chromosomes are generated. Each virtual chromosome contains a sequence of numeric values. Positions in the sequences in the virtual chromosomes do not correspond to only one respective distinct parameter in the set of parameters to be optimized. Based on the virtual chromosomes, many new chromosomes are generated. Each new chromosome contains a sequence of values. Each position in the sequences in the new chromosomes corresponds to only one respective distinct parameter in the set of parameters to be optimized. The computer may be configured based on a best new chromosome.

公开(公告)号：US20220366297A1

公开(公告)日：2022-11-17

申请号：US17319729

申请日：2021-05-13

Applicant: Oracle International Corporation

Inventor： Yasha Pushak ,  Zahra Zohrevand ,  Tayler Hetherington ,  Karoon Rashedi Nia ,  Sanjay Jinturkar ,  Nipun Agarwal

IPC: G06N20/00 ,  G06N5/04 ,  G06K9/62

Abstract: In an embodiment, a computer hosts a machine learning (ML) model that infers a particular inference for a particular tuple that is based on many features. For each feature, and for each of many original tuples, the computer: a) randomly selects many perturbed values from original values of the feature in the original tuples, b) generates perturbed tuples that are based on the original tuple and a respective perturbed value, c) causes the ML model to infer a respective perturbed inference for each perturbed tuple, and d) measures a respective difference between each perturbed inference of the perturbed tuples and the particular inference. For each feature, a respective importance of the feature is calculated based on the differences measured for the feature. Feature importances may be used to rank features by influence and/or generate a local ML explainability (MLX) explanation.

公开(公告)号：US20250139474A1

公开(公告)日：2025-05-01

申请号：US18544899

申请日：2023-12-19

Applicant: Oracle International Corporation

Inventor： Yasha Pushak ,  Ehsan Soltan Aghai ,  Hesam Fathi Moghadam ,  Sungpack Hong ,  Hassan Chafi

IPC: G06N7/01 ,  G06N5/04

Abstract: A computer obtains multipliers of a sensitive feature. From an input that contains a value of the feature, a probability of a class is inferred. Based on the value of the feature in the input, one of the multipliers of the feature is selected. The multiplier is specific to both of the feature and the value of the feature. The input is classified based on a multiplicative product of the probability of the class and the multiplier that is specific to both of the feature and the value of the feature. In an embodiment, a black-box tri-objective optimizer generates multipliers on a three-way Pareto frontier from which a user may interactively select a combination of multipliers that provides a best three-way tradeoff between fairness and accuracy. The optimizer has three objectives to respectively optimize three distinct validation metrics that may, for example, be accuracy, fairness, and favorable outcome rate decrease.

公开(公告)号：US12217136B2

公开(公告)日：2025-02-04

申请号：US16935313

申请日：2020-07-22

Applicant: Oracle International Corporation

Inventor： Felix Schmidt ,  Yasha Pushak ,  Stuart Wray

IPC: G06N20/00 ,  G06F16/901 ,  G06N5/04

Abstract: Techniques are described that extend supervised machine-learning algorithms for use with semi-supervised training. Random labels are assigned to unlabeled training data, and the data is split into k partitions. During a label-training iteration, each of these k partitions is combined with the labeled training data, and the combination is used train a single instance of the machine-learning model. Each of these trained models are then used to predict labels for data points in the k−1 partitions of previously-unlabeled training data that were not used to train of the model. Thus, every data point in the previously-unlabeled training data obtains k−1 predicted labels. For each data point, these labels are aggregated to obtain a composite label prediction for the data point. After the labels are determined via one or more label-training iterations, a machine-learning model is trained on data with the resulting composite label predictions and on the labeled data set.

公开(公告)号：US20240394557A1

公开(公告)日：2024-11-28

申请号：US18202472

申请日：2023-05-26

Applicant: Oracle International Corporation

Inventor： Yasha Pushak ,  Mobina Mahdavi ,  Ali Asgari Khoshouyeh ,  Ali Seyfi ,  Zahra Zohrevand ,  Ritesh Ahuja ,  Moein Owhadi Kareshk ,  Hesam Fathi Moghadam ,  Sungpack Hong ,  Hassan Chafi

IPC: G06N20/00

Abstract: In an embodiment, a computer combines first original hyperparameters and second original hyperparameters into combined hyperparameters. In each iteration of a binary search that selects hyperparameters, these are selected: a) important hyperparameters from the combined hyperparameters and b) based on an estimated complexity decrease by including only important hyperparameters as compared to the combined hyperparameters, which only one boundary of the binary search to adjust. For the important hyperparameters of a last iteration of the binary search that selects hyperparameters, a pruned value range of a particular hyperparameter is generated based on a first original value range of the particular hyperparameter for the first original hyperparameters and a second original value range of the same particular hyperparameter for the second original hyperparameters. To accelerate hyperparameter optimization (HPO), the particular hyperparameter is tuned only within the pruned value range to discover an optimal value for configuring and training a machine learning model.

公开(公告)号：US20230376366A1

公开(公告)日：2023-11-23

申请号：US17992743

申请日：2022-11-22

Applicant: Oracle International Corporation

Inventor： Ali Seyfi ,  Yasha Pushak ,  Sungpack Hong ,  Hesam Fathi Moghadam ,  Hassan Chafi

IPC: G06F11/00 ,  G06N20/00

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.