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You searched for +publisher:"University of Arkansas" +contributor:("Xintao Wu"). Showing records 1 – 2 of 2 total matches.

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University of Arkansas

1. Wu, Yongkai. Achieving Causal Fairness in Machine Learning.

Degree: PhD, 2020, University of Arkansas

Fairness is a social norm and a legal requirement in today's society. Many laws and regulations (e.g., the Equal Credit Opportunity Act of 1974) have been established to prohibit discrimination and enforce fairness on several grounds, such as gender, age, sexual orientation, race, and religion, referred to as sensitive attributes. Nowadays machine learning algorithms are extensively applied to make important decisions in many real-world applications, e.g., employment, admission, and loans. Traditional machine learning algorithms aim to maximize predictive performance, e.g., accuracy. Consequently, certain groups may get unfairly treated when those algorithms are applied for decision-making. Therefore, it is an imperative task to develop fairness-aware machine learning algorithms such that the decisions made by them are not only accurate but also subject to fairness requirements. In the literature, machine learning researchers have proposed association-based fairness notions, e.g., statistical parity, disparate impact, equality of opportunity, etc., and developed respective discrimination mitigation approaches. However, these works did not consider that fairness should be treated as a causal relationship. Although it is well known that association does not imply causation, the gap between association and causation is not paid sufficient attention by the fairness researchers and stakeholders. The goal of this dissertation is to study fairness in machine learning, define appropriate fairness notions, and develop novel discrimination mitigation approaches from a causal perspective. Based on Pearl's structural causal model, we propose to formulate discrimination as causal effects of the sensitive attribute on the decision. We consider different types of causal effects to cope with different situations, including the path-specific effect for direct/indirect discrimination, the counterfactual effect for group/individual discrimination, and the path-specific counterfactual effect for general cases. In the attempt to measure discrimination, the unidentifiable situations pose an inevitable barrier to the accurate causal inference. To address this challenge, we propose novel bounding methods to accurately estimate the strength of unidentifiable fairness notions, including path-specific fairness, counterfactual fairness, and path-specific counterfactual fairness. Based on the estimation of fairness, we develop novel and efficient algorithms for learning fair classification models. Besides classification, we also investigate the discrimination issues in other machine learning scenarios, such as ranked data analysis. Advisors/Committee Members: Xintao Wu, Qinghua Li, Brajendra Panda.

Subjects/Keywords: Algorithmic Bias; Causal Inference; Fairness; Machine Learning; Artificial Intelligence and Robotics; Theory and Algorithms

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wu, Y. (2020). Achieving Causal Fairness in Machine Learning. (Doctoral Dissertation). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/3632

Chicago Manual of Style (16th Edition):

Wu, Yongkai. “Achieving Causal Fairness in Machine Learning.” 2020. Doctoral Dissertation, University of Arkansas. Accessed October 25, 2020. https://scholarworks.uark.edu/etd/3632.

MLA Handbook (7th Edition):

Wu, Yongkai. “Achieving Causal Fairness in Machine Learning.” 2020. Web. 25 Oct 2020.

Vancouver:

Wu Y. Achieving Causal Fairness in Machine Learning. [Internet] [Doctoral dissertation]. University of Arkansas; 2020. [cited 2020 Oct 25]. Available from: https://scholarworks.uark.edu/etd/3632.

Council of Science Editors:

Wu Y. Achieving Causal Fairness in Machine Learning. [Doctoral Dissertation]. University of Arkansas; 2020. Available from: https://scholarworks.uark.edu/etd/3632


University of Arkansas

2. Zheng, Panpan. Dynamic Fraud Detection via Sequential Modeling.

Degree: PhD, 2020, University of Arkansas

The impacts of information revolution are omnipresent from life to work. The web services have signicantly changed our living styles in daily life, such as Facebook for communication and Wikipedia for knowledge acquirement. Besides, varieties of information systems, such as data management system and management information system, make us work more eciently. However, it is usually a double-edged sword. With the popularity of web services, relevant security issues are arising, such as fake news on Facebook and vandalism on Wikipedia, which denitely impose severe security threats to OSNs and their legitimate participants. Likewise, oce automation incurs another challenging security issue, insider threat, which may involve the theft of condential information, the theft of intellectual property, or the sabotage of computer systems. A recent survey says that 27% of all cyber crime incidents are suspected to be committed by the insiders. As a result, how to ag out these malicious web users or insiders is urgent. The fast development of machine learning (ML) techniques oers an unprecedented opportunity to build some ML models that can assist humans to detect the individuals who conduct misbehaviors automatically. However, unlike some static outlier detection scenarios where ML models have achieved promising performance, the malicious behaviors conducted by humans are often dynamic. Such dynamic behaviors lead to various unique challenges of dynamic fraud detection: Unavailability of sucient labeled data - traditional machine learning approaches usually require a balanced training dataset consisting of normal and abnormal samples. In practice, however, there are far fewer abnormal labeled samples than normal ones. Lack of high quality labels - the labeled training records often have the time gap between the time that fraudulent users commit fraudulent actions and the time that they are suspended by the platforms. Time-evolving nature - users are always changing their behaviors over time. To address the aforementioned challenges, in this dissertation, we conduct a systematic study for dynamic fraud detection, with a focus on: (1) Unavailability of labeled data: we present (a) a few-shot learning framework to handle the extremely imbalanced dataset that abnormal samples are far fewer than the normal ones and (b) a one-class fraud detection method using a complementary GAN (Generative Adversarial Network) to adaptively generate potential abnormal samples; (2) Lack of high-quality labels: we develop a neural survival analysis model for fraud early detection to deal with the time gap; (3) Time-evolving nature: we propose (a) a hierarchical neural temporal point process model and (b) a dynamic Dirichlet marked Hawkes process model for fraud detection. Advisors/Committee Members: Xintao Wu, Qinghua Li, Song Yang.

Subjects/Keywords: Dirichlet process; Fraud Detection; Machine Learning; Mixture Model; Sequential Model; Survival Analysis; Databases and Information Systems; Information Security

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Zheng, P. (2020). Dynamic Fraud Detection via Sequential Modeling. (Doctoral Dissertation). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/3633

Chicago Manual of Style (16th Edition):

Zheng, Panpan. “Dynamic Fraud Detection via Sequential Modeling.” 2020. Doctoral Dissertation, University of Arkansas. Accessed October 25, 2020. https://scholarworks.uark.edu/etd/3633.

MLA Handbook (7th Edition):

Zheng, Panpan. “Dynamic Fraud Detection via Sequential Modeling.” 2020. Web. 25 Oct 2020.

Vancouver:

Zheng P. Dynamic Fraud Detection via Sequential Modeling. [Internet] [Doctoral dissertation]. University of Arkansas; 2020. [cited 2020 Oct 25]. Available from: https://scholarworks.uark.edu/etd/3633.

Council of Science Editors:

Zheng P. Dynamic Fraud Detection via Sequential Modeling. [Doctoral Dissertation]. University of Arkansas; 2020. Available from: https://scholarworks.uark.edu/etd/3633

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