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    MITIGATING BIAS IN MACHINE LEARNING WITHOUT POSITIVE OUTCOME RATE REGRESSIONS
    61.
    发明申请
    MITIGATING BIAS IN MACHINE LEARNING WITHOUT POSITIVE OUTCOME RATE REGRESSIONS 有权

    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.

    MEMORY-TRACKING RESOURCE MANAGER FOR ELASTIC DISTRIBUTED GRAPH-PROCESSING SYSTEM
    63.
    发明申请
    MEMORY-TRACKING RESOURCE MANAGER FOR ELASTIC DISTRIBUTED GRAPH-PROCESSING SYSTEM 有权

    公开(公告)号：US20250094224A1

    公开(公告)日：2025-03-20

    申请号：US18369254

    申请日：2023-09-18

    Applicant: Oracle International Corporation

    Inventor： Calin Iorgulescu ,  Martin Kucera ,  Vasileios Trigonakis ,  Arnaud Delamare ,  Petr Koupy ,  Jinsu Lee ,  Sungpack Hong ,  Hassan Chafi

    IPC: G06F9/50 ,  G06F9/48

    Abstract: A resource manager tracks the amount of available memory for a cluster of machines and for each machine in the cluster. The resource manager receives a reservation request from a job for a graph processing operation. The reservation request specifies an identification of the job, a type of reservation, and an amount of memory requested. The resource manager determines whether to grant the reservation request based on the type of reservation, the amount of memory requested, and the amount of available memory in the cluster or in one or more machines in the cluster. In response to determining to grant the reservation request, the resource manager sends a response to the job indicating an amount of memory reserved and adjusts the amount of available cluster memory and the amount of available machine memory for at least one machine in the cluster based on the amount of memory reserved.

    ACCELERATING AUTOMATED ALGORITHM CONFIGURATION USING HISTORICAL PERFORMANCE DATA
    64.
    发明申请
    ACCELERATING AUTOMATED ALGORITHM CONFIGURATION USING HISTORICAL PERFORMANCE DATA 有权

    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.

    FAST AND ACCURATE ANOMALY DETECTION EXPLANATIONS WITH FORWARD-BACKWARD FEATURE IMPORTANCE
    66.
    发明公开
    FAST AND ACCURATE ANOMALY DETECTION EXPLANATIONS WITH FORWARD-BACKWARD FEATURE IMPORTANCE 审中-公开

    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.

    DETERMINISTIC SEMANTIC FOR GRAPH PROPERTY UPDATE QUERIES AND ITS EFFICIENT IMPLEMENTATION
    68.
    发明申请
    DETERMINISTIC SEMANTIC FOR GRAPH PROPERTY UPDATE QUERIES AND ITS EFFICIENT IMPLEMENTATION 有权

    公开(公告)号：US20230095703A1

    公开(公告)日：2023-03-30

    申请号：US17479006

    申请日：2021-09-20

    Applicant: Oracle International Corporation

    Inventor： Bence Czipo ,  Vlad Ioan Haprian ,  Oskar Van Rest ,  Damien Hilloulin ,  Vasileios Trigonakis ,  Yahya Ez-zainabi ,  Sungpack Hong ,  Hassan Chafi

    IPC: G06F16/23 ,  G06F11/07

    Abstract: Efficiently implemented herein is a deterministic semantic for property updates by graph queries. Mechanisms of determinism herein ensure data consistency for graph mutation. These mechanisms facilitate optimistic execution of graph access despite a potential data access conflict. This approach may include various combinations of special activities such as detecting potential conflicts during query compile time, applying query transformations to eliminate those conflicts during code generation where possible, and executing updates in an optimistic way that safely fails if determinism cannot be guaranteed. In an embodiment, a computer receives a request to modify a graph. The request to modify the graph is optimistically executed after preparation and according to safety precautions as presented herein. Based on optimistically executing the request, a data access conflict actually occurs and is automatically detected. Based on the data access conflict, optimistically executing the request is prematurely and automatically halted without finishing executing the request.

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