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- Genre: Doctoral Dissertation
- Creators: Liu, Huan
Description
This dissertation considers the question of how convenient access to copious networked observational data impacts our ability to learn causal knowledge. It investigates in what ways learning causality from such data is different from -- or the same as -- the traditional causal inference which often deals with small scale i.i.d. data collected from randomized controlled trials? For example, how can we exploit network information for a series of tasks in the area of learning causality? To answer this question, the dissertation is written toward developing a suite of novel causal learning algorithms that offer actionable insights for a series of causal inference tasks with networked observational data. The work aims to benefit real-world decision-making across a variety of highly influential applications. In the first part of this dissertation, it investigates the task of inferring individual-level causal effects from networked observational data. First, it presents a representation balancing-based framework for handling the influence of hidden confounders to achieve accurate estimates of causal effects. Second, it extends the framework with an adversarial learning approach to properly combine two types of existing heuristics: representation balancing and treatment prediction. The second part of the dissertation describes a framework for counterfactual evaluation of treatment assignment policies with networked observational data. A novel framework that captures patterns of hidden confounders is developed to provide more informative input for downstream counterfactual evaluation methods. The third part presents a framework for debiasing two-dimensional grid-based e-commerce search with observational search log data where there is an implicit network connecting neighboring products in a search result page. A novel inverse propensity scoring framework that models user behavior patterns for two-dimensional display in e-commerce websites is developed, which aims to optimize online performance of ranking algorithms with offline log data.
ContributorsGuo, Ruocheng (Author) / Liu, Huan (Thesis advisor) / Candan, K. Selcuk (Committee member) / Xue, Guoliang (Committee member) / Kiciman, Emre (Committee member) / Arizona State University (Publisher)
Created2021
Description
This dissertation constructs a new computational processing framework to robustly and precisely quantify retinotopic maps based on their angle distortion properties. More generally, this framework solves the problem of how to robustly and precisely quantify (angle) distortions of noisy or incomplete (boundary enclosed) 2-dimensional surface to surface mappings. This framework builds upon the Beltrami Coefficient (BC) description of quasiconformal mappings that directly quantifies local mapping (circles to ellipses) distortions between diffeomorphisms of boundary enclosed plane domains homeomorphic to the unit disk. A new map called the Beltrami Coefficient Map (BCM) was constructed to describe distortions in retinotopic maps. The BCM can be used to fully reconstruct the original target surface (retinal visual field) of retinotopic maps. This dissertation also compared retinotopic maps in the visual processing cascade, which is a series of connected retinotopic maps responsible for visual data processing of physical images captured by the eyes. By comparing the BCM results from a large Human Connectome project (HCP) retinotopic dataset (N=181), a new computational quasiconformal mapping description of the transformed retinal image as it passes through the cascade is proposed, which is not present in any current literature. The description applied on HCP data provided direct visible and quantifiable geometric properties of the cascade in a way that has not been observed before. Because retinotopic maps are generated from in vivo noisy functional magnetic resonance imaging (fMRI), quantifying them comes with a certain degree of uncertainty. To quantify the uncertainties in the quantification results, it is necessary to generate statistical models of retinotopic maps from their BCMs and raw fMRI signals. Considering that estimating retinotopic maps from real noisy fMRI time series data using the population receptive field (pRF) model is a time consuming process, a convolutional neural network (CNN) was constructed and trained to predict pRF model parameters from real noisy fMRI data
ContributorsTa, Duyan Nguyen (Author) / Wang, Yalin (Thesis advisor) / Lu, Zhong-Lin (Committee member) / Hansford, Dianne (Committee member) / Liu, Huan (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Machine learning models and in specific, neural networks, are well known for being inscrutable in nature. From image classification tasks and generative techniques for data augmentation, to general purpose natural language models, neural networks are currently the algorithm of preference that is riding the top of the current artificial intelligence (AI) wave, having experienced the greatest boost in popularity above any other machine learning solution. However, due to their inscrutable design based on the optimization of millions of parameters, it is ever so complex to understand how their decision is influenced nor why (and when) they fail. While some works aim at explaining neural network decisions or making systems to be inherently interpretable the great majority of state of the art machine learning works prioritize performance over interpretability effectively becoming black boxes. Hence, there is still uncertainty in the decision boundaries of these already deployed solutions whose predictions should still be analyzed and taken with care. This becomes even more important when these models are used on sensitive scenarios such as medicine, criminal justice, settings with native inherent social biases or where egregious mispredictions can negatively impact the system or human trust down the line. Thus, the aim of this work is to provide a comprehensive analysis on the failure modes of the state of the art neural networks from three domains: large image classifiers and their misclassifications, generative adversarial networks when used for data augmentation and transformer networks applied to structured representations and reasoning about actions and change.
ContributorsOlmo Hernandez, Alberto (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Li, Baoxin (Committee member) / Sengupta, Sailik (Committee member) / Arizona State University (Publisher)
Created2022
Description
With the bloom of machine learning, a massive amount of data has been used in the training process of machine learning. A tremendous amount of this data is user-generated data which allows the machine learning models to produce accurate results and personalized services. Nevertheless, I recognize the importance of preserving the privacy of individuals by protecting their information in the training process. One privacy attack that affects individuals is the private attribute inference attack. The private attribute attack is the process of inferring individuals' information that they do not explicitly reveal, such as age, gender, location, and occupation. The impacts of this go beyond knowing the information as individuals face potential risks. Furthermore, some applications need sensitive data to train the models and predict helpful insights and figuring out how to build privacy-preserving machine learning models will increase the capabilities of these applications.However, improving privacy affects the data utility which leads to a dilemma between privacy and utility. The utility of the data is measured by the quality of the data for different tasks. This trade-off between privacy and utility needs to be maintained to satisfy the privacy requirement and the result quality. To achieve more scalable privacy-preserving machine learning models, I investigate the privacy risks that affect individuals' private information in distributed machine learning. Even though the distributed machine learning has been driven by privacy concerns, privacy issues have been
proposed in the literature which threaten individuals' privacy. In this dissertation, I investigate how to measure and protect individuals' privacy in centralized and distributed machine learning models. First, a privacy-preserving text representation learning is proposed to protect users' privacy that can be revealed from user generated data. Second, a novel privacy-preserving text classification for split learning is presented to improve users' privacy and retain high utility by defending against private attribute inference attacks.
ContributorsAlnasser, Walaa (Author) / Liu, Huan (Thesis advisor) / Davulcu, Hasan (Committee member) / Shu, Kai (Committee member) / Bao, Tiffany (Committee member) / Arizona State University (Publisher)
Created2022
Description
Social media platforms provide a rich environment for analyzing user behavior. Recently, deep learning-based methods have been a mainstream approach for social media analysis models involving complex patterns. However, these methods are susceptible to biases in the training data, such as participation inequality. Basically, a mere 1% of users generate the majority of the content on social networking sites, while the remaining users, though engaged to varying degrees, tend to be less active in content creation and largely silent. These silent users consume and listen to information that is propagated on the platform.However, their voice, attitude, and interests are not reflected in the online content, making the decision of the current methods predisposed towards the opinion of the active users. So models can mistake the loudest users for the majority. To make the silent majority heard is to reveal the true landscape of the platform. In this dissertation, to compensate for this bias in the data, which is related to user-level data scarcity, I introduce three pieces of research work. Two of these proposed solutions deal with the data on hand while the other tries to augment the current data. Specifically, the first proposed approach modifies the weight of users' activity/interaction in the input space, while the second approach involves re-weighting the loss based on the users' activity levels during the downstream task training. Lastly, the third approach uses large language models (LLMs) and learns the user's writing behavior to expand the current data. In other words, by utilizing LLMs as a sophisticated knowledge base, this method aims to augment the silent user's data.
ContributorsKarami, Mansooreh (Author) / Liu, Huan (Thesis advisor) / Sen, Arunabha (Committee member) / Davulcu, Hasan (Committee member) / Mancenido, Michelle V. (Committee member) / Arizona State University (Publisher)
Created2023
Description
Machine learning models are increasingly being deployed in real-world applications where their predictions are used to make critical decisions in a variety of domains. The proliferation of such models has led to a burgeoning need to ensure the reliability and safety of these models, given the potential negative consequences of model vulnerabilities. The complexity of machine learning models, along with the extensive data sets they analyze, can result in unpredictable and unintended outcomes. Model vulnerabilities may manifest due to errors in data input, algorithm design, or model deployment, which can have significant implications for both individuals and society. To prevent such negative outcomes, it is imperative to identify model vulnerabilities at an early stage in the development process. This will aid in guaranteeing the integrity, dependability, and safety of the models, thus mitigating potential risks and enabling the full potential of these technologies to be realized. However, enumerating vulnerabilities can be challenging due to the complexity of the real-world environment. Visual analytics, situated at the intersection of human-computer interaction, computer graphics, and artificial intelligence, offers a promising approach for achieving high interpretability of complex black-box models, thus reducing the cost of obtaining insights into potential vulnerabilities of models. This research is devoted to designing novel visual analytics methods to support the identification and analysis of model vulnerabilities. Specifically, generalizable visual analytics frameworks are instantiated to explore vulnerabilities in machine learning models concerning security (adversarial attacks and data perturbation) and fairness (algorithmic bias). In the end, a visual analytics approach is proposed to enable domain experts to explain and diagnose the model improvement of addressing identified vulnerabilities of machine learning models in a human-in-the-loop fashion. The proposed methods hold the potential to enhance the security and fairness of machine learning models deployed in critical real-world applications.
ContributorsXie, Tiankai (Author) / Maciejewski, Ross (Thesis advisor) / Liu, Huan (Committee member) / Bryan, Chris (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Created2023
Description
An important objective of AI is to understand real-world observations and build up interactive communication with people. The ability to interpret and react to the perception reveals the important necessity of developing such a system across both the modalities of Vision (V) and Language (L). Although there have been massive efforts on various VL tasks, e.g., Image/Video Captioning, Visual Question Answering, and Textual Grounding, very few of them focus on building the VL models with increased efficiency under real-world scenarios. The main focus of this dissertation is to comprehensively investigate the very uncharted efficient VL learning, aiming to build lightweight, data-efficient, and real-world applicable VL models. The proposed studies in this dissertation take three primary aspects into account when it comes to efficient VL, 1). Data Efficiency: collecting task-specific annotations is prohibitively expensive and so manual labor is not always attainable. Techniques are developed to assist the VL learning from implicit supervision, i.e., in a weakly- supervised fashion. 2). Continuing from that, efficient representation learning is further explored with increased scalability, leveraging a large image-text corpus without task-specific annotations. In particular, the knowledge distillation technique is studied for generic Representation Learning which proves to bring substantial performance gain to the regular representation learning schema. 3). Architectural Efficiency. Deploying the VL model on edge devices is notoriously challenging due to their cumbersome architectures. To further extend these advancements to the real world, a novel efficient VL architecture is designed to tackle the inference bottleneck and the inconvenient two-stage training. Extensive discussions have been conducted on several critical aspects that prominently influence the performances of compact VL models.
ContributorsFang, Zhiyuan (Author) / Yang, Yezhou (Thesis advisor) / Baral, Chitta (Committee member) / Liu, Huan (Committee member) / Liu, Zicheng (Committee member) / Arizona State University (Publisher)
Created2022
Description
The problem of monitoring complex networks for the detection of anomalous behavior is well known. Sensors are usually deployed for the purpose of monitoring these networks for anomalies and Sensor Placement Optimization (SPO) is the problem of determining where these sensors should be placed (deployed) in the network. Prior works have utilized the well known Set Cover formulation in order to determine the locations where sensors should be placed in the network, so that anomalies can be effectively detected. However, such works cannot be utilized to address the problem when the objective is to not only detect the presence of anomalies, but also to detect (distinguish) the source(s) of the detected anomalies, i.e., uniquely monitoring the network. In this dissertation, I attempt to fill in this gap by utilizing the mathematical concept of Identifying Codes and illustrating how it not only can overcome the aforementioned limitation, but also it, and its variants, can be utilized to monitor complex networks modeled from multiple domains. Over the course of this dissertation, I make key contributions which further enhance the efficacy and applicability of Identifying Codes as a monitoring strategy. First, I show how Identifying Codes are superior to not only the Set Cover formulation but also standard graph centrality metrics, for the purpose of uniquely monitoring complex networks. Second, I study novel problems such as the budget constrained Identifying Code, scalable Identifying Code, robust Identifying Code etc., and present algorithms and results for the respective problems. Third, I present useful Identifying Code results for restricted graph classes such as Unit Interval Bigraphs and Unit Disc Bigraphs. Finally, I show the universality of Identifying Codes by applying it to multiple domains.
ContributorsBasu, Kaustav (Author) / Sen, Arunabha (Thesis advisor) / Davulcu, Hasan (Committee member) / Liu, Huan (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2022
Description
Data integration involves the reconciliation of data from diverse data sources in order to obtain a unified data repository, upon which an end user such as a data analyst can run analytics sessions to explore the data and obtain useful insights. Supervised Machine Learning (ML) for data integration tasks such as ontology (schema) or entity (instance) matching requires several training examples in terms of manually curated, pre-labeled matching and non-matching schema concept or entity pairs which are hard to obtain. On similar lines, an analytics system without predictive capabilities about the impending workload can incur huge querying latencies, while leaving the onus of understanding the underlying database schema and writing a meaningful query at every step during a data exploration session on the user. In this dissertation, I will describe the human-in-the-loop Machine Learning (ML) systems that I have built towards data integration and predictive analytics. I alleviate the need for extensive prior labeling by utilizing active learning (AL) for dataintegration. In each AL iteration, I detect the unlabeled entity or schema concept pairs that would strengthen the ML classifier and selectively query the human oracle for such labels in a budgeted fashion. Thus, I make use of human assistance for ML-based data integration. On the other hand, when the human is an end user exploring data through Online Analytical Processing (OLAP) queries, my goal is to pro-actively assist the human by predicting the top-K next queries that s/he is likely to be interested in. I will describe my proposed SQL-predictor, a Business Intelligence (BI) query predictor and a geospatial query cardinality estimator with an emphasis on schema abstraction, query representation and how I adapt the ML models for these tasks. For each system, I will discuss the evaluation metrics and how the proposed systems compare to the state-of-the-art baselines on multiple datasets and query workloads.
ContributorsMeduri, Venkata Vamsikrishna (Author) / Sarwat, Mohamed (Thesis advisor) / Bryan, Chris (Committee member) / Liu, Huan (Committee member) / Ozcan, Fatma (Committee member) / Popa, Lucian (Committee member) / Arizona State University (Publisher)
Created2022
Description
This dissertation investigates the problem of efficiently and effectively prioritizing a vulnerability risk in a computer networking system. Vulnerability prioritization is one of the most challenging issues in vulnerability management, which affects allocating preventive and defensive resources in a computer networking system. Due to the large number of identified vulnerabilities, it is very challenging to remediate them all in a timely fashion. Thus, an efficient and effective vulnerability prioritization framework is required. To deal with this challenge, this dissertation proposes a novel risk-based vulnerability prioritization framework that integrates the recent artificial intelligence techniques (i.e., neuro-symbolic computing and logic reasoning). The proposed work enhances the vulnerability management process by prioritizing vulnerabilities with high risk by refining the initial risk assessment with the network constraints. This dissertation is organized as follows. The first part of this dissertation presents the overview of the proposed risk-based vulnerability prioritization framework, which contains two stages. The second part of the dissertation investigates vulnerability risk features in a computer networking system. The third part proposes the first stage of this framework, a vulnerability risk assessment model. The proposed assessment model captures the pattern of vulnerability risk features to provide a more comprehensive risk assessment for a vulnerability. The fourth part proposes the second stage of this framework, a vulnerability prioritization reasoning engine. This reasoning engine derives network constraints from interactions between vulnerabilities and network environment elements based on network and system setups. This proposed framework assesses a vulnerability in a computer networking system based on its actual security impact by refining the initial risk assessment with the network constraints.
ContributorsZeng, Zhen (Author) / Xue, Guoliang (Thesis advisor) / Liu, Huan (Committee member) / Zhao, Ming (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2022