公开(公告)号：US20230244925A1

公开(公告)日：2023-08-03

申请号：US17589595

申请日：2022-01-31

Applicant: salesforce.com, inc.

Inventor： Wenzhuo Yang ,  Chu Hong Hoi ,  Kun Zhang

IPC: G06N3/08 ,  G06K9/62

CPC classification number: G06N3/08 ,  G06K9/6284 ,  G06K9/6262

Abstract: Embodiments described herein provide a system and method for unsupervised anomaly detection. The system receives, via a communication interface, a dataset of instances that include anomalies. The system determines, via an inlier model, a set of noisy labels. The system trains a causality-based label-noise model based at least in part on the set of noisy labels and the set of high-confidence instances. The system determines an estimated proportion of anomalies in the dataset of instances. The system retrains the inlier model based on the estimated inlier samples. The system iteratively retrains the inlier model and the trained causality-based label-noise model based on the output from the corresponding retrained models not converging within the convergence threshold. The system extracts the anomaly detection model from the iteratively trained causality-based label-noise model.

公开(公告)号：US20220114464A1

公开(公告)日：2022-04-14

申请号：US17162967

申请日：2021-01-29

Applicant: salesforce.com, inc.

Inventor： Wenzhuo Yang ,  Jia Li ,  Chu Hong Hoi ,  Caiming Xiong

IPC: G06N5/04 ,  G06N20/00 ,  G06N7/00 ,  G06N5/00

Abstract: Embodiments described herein provide a two-stage model-agnostic approach for generating counterfactual explanation via counterfactual feature selection and counterfactual feature optimization. Given a query instance, counterfactual feature selection picks a subset of feature columns and values that can potentially change the prediction and then counterfactual feature optimization determines the best feature value for the selected feature as a counterfactual example.

公开(公告)号：US20220382856A1

公开(公告)日：2022-12-01

申请号：US17514487

申请日：2021-10-29

Applicant: salesforce.com, inc.

Inventor： Wenzhuo Yang ,  Chu Hong Hoi ,  Kun Zhang

IPC: G06F21/55 ,  G06N20/00

Abstract: Embodiments described herein provide a causality-based anomaly detection mechanism that formulates multivariate time series as instances that do not follow the regular causal mechanism. Specifically, the causality-based anomaly detection mechanism leverages the causal structure discovered from data so that the joint distribution of multivariate time series is factorized into simpler modules where each module corresponds to a local causal mechanism, reflected by the corresponding conditional distribution. Those local mechanisms are modular or autonomous and can then be handled separately. In light of this modularity property, the anomaly detection problem then naturally decomposed into a series of low-dimensional anomaly detection problems. Each sub-problem is concerned with a local mechanism.

公开(公告)号：US20220114481A1

公开(公告)日：2022-04-14

申请号：US17162931

申请日：2021-01-29

Applicant: salesforce.com, inc.

Inventor： Wenzhuo Yang ,  Jia Li ,  Chu Hong Hoi ,  Caiming Xiong

IPC: G06N20/00 ,  G06K9/62 ,  G06N7/00

Abstract: Embodiments described herein provide a two-stage model-agnostic approach for generating counterfactual explanation via counterfactual feature selection and counterfactual feature optimization. Given a query instance, counterfactual feature selection picks a subset of feature columns and values that can potentially change the prediction and then counterfactual feature optimization determines the best feature value for the selected feature as a counterfactual example.

公开(公告)号：US20210374132A1

公开(公告)日：2021-12-02

申请号：US17093885

申请日：2020-11-10

Applicant: salesforce.com, inc.

Inventor： Wenzhuo Yang ,  Jia Li ,  Chenxi Li ,  Latrice Barnett ,  Markus Anderle ,  Simo Arajarvi ,  Harshavardhan Utharavalli ,  Caiming Xiong ,  Richard Socher ,  Chu Hong Hoi

IPC: G06F16/2457 ,  G06N20/20

Abstract: Embodiments are directed to a machine learning recommendation system. The system receives a user query for generating a recommendation for one or more items with an explanation associated with recommending the one or more items. The system obtains first features of at least one user and second features of a set of items. The system provides the first features and the second features to a first machine learning network for determining a predicted score for an item. The system provides a portion of the first features and a portion of the second features to second machine learning networks for determining explainability scores for an item and generating corresponding explanation narratives. The system provides the recommendation for one or more items and corresponding explanation narratives based on ranking predicted scores and explainability scores for the items.