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Articles 1 - 6 of 6
Full-Text Articles in Computer Sciences
Enhancing Usability And Explainability Of Data Systems, Anna Fariha
Enhancing Usability And Explainability Of Data Systems, Anna Fariha
Doctoral Dissertations
The recent growth of data science expanded its reach to an ever-growing user base of nonexperts, increasing the need for usability, understandability, and explainability in these systems. Enhancing usability makes data systems accessible to people with different skills and backgrounds alike, leading to democratization of data systems. Furthermore, proper understanding of data and data-driven systems is necessary for the users to trust the function of the systems that learn from data. Finally, data systems should be transparent: when a data system behaves unexpectedly or malfunctions, the users deserve proper explanation of what caused the observed incident. Unfortunately, …
History Modeling For Conversational Information Retrieval, Chen Qu
History Modeling For Conversational Information Retrieval, Chen Qu
Doctoral Dissertations
Conversational search is an embodiment of an iterative and interactive approach to information retrieval (IR) that has been studied for decades. Due to the recent rise of intelligent personal assistants, such as Siri, Alexa, AliMe, Cortana, and Google Assistant, a growing part of the population is moving their information-seeking activities to voice- or text-based conversational interfaces. One of the major challenges of conversational search is to leverage the conversation history to understand and fulfill the users' information needs. In this dissertation work, we investigate history modeling approaches for conversational information retrieval. We start from history modeling for user intent prediction. …
Enabling Declarative And Scalable Prescriptive Analytics In Relational Data, Matteo Brucato
Enabling Declarative And Scalable Prescriptive Analytics In Relational Data, Matteo Brucato
Doctoral Dissertations
Constrained optimization problems are at the heart of significant applications in a broad range of domains, including finance, transportation, manufacturing, and healthcare. They are often found at the final step of business analytics, namely prescriptive analytics, to allow businesses to transform a rich understanding of data, typically provided by advanced predictive models, into actionable decisions. Modeling and solving these problems has relied on application-specific solutions, which are often complex, error-prone, and do not generalize. Our goal is to create a domain-independent, declarative approach, supported and powered by the system where the data relevant to these problems typically resides: the database. …
Neural Approaches To Feedback In Information Retrieval, Keping Bi
Neural Approaches To Feedback In Information Retrieval, Keping Bi
Doctoral Dissertations
Relevance feedback on search results indicates users' search intent and preferences. Extensive studies have shown that incorporating relevance feedback (RF) on the top k (usually 10) ranked results significantly improves the performance of re-ranking. However, most existing research on user feedback focuses on words-based retrieval models. Recently, neural retrieval models have shown their efficacy in capturing relevance matching in retrieval but little research has been conducted on neural approaches to feedback. This leads us to study different aspects of feedback with neural approaches in the dissertation. RF techniques are seldom used in real search scenarios since they can require significant …
Towards Practical Differentially Private Mechanism Design And Deployment, Dan Zhang
Towards Practical Differentially Private Mechanism Design And Deployment, Dan Zhang
Doctoral Dissertations
As the collection of personal data has increased, many institutions face an urgent need for reliable protection of sensitive data. Among the emerging privacy protection mechanisms, differential privacy offers a persuasive and provable assurance to individuals and has become the dominant model in the research community. However, despite growing adoption, the complexity of designing differentially private algorithms and effectively deploying them in real-world applications remains high. In this thesis, we address two main questions: 1) how can we aid programmers in developing private programs with high utility? and 2) how can we deploy differentially private algorithms to visual analytics systems? …
Neural Methods For Answer Passage Retrieval Over Sparse Collections, Daniel Cohen
Neural Methods For Answer Passage Retrieval Over Sparse Collections, Daniel Cohen
Doctoral Dissertations
Recent advances in machine learning have allowed information retrieval (IR) techniques to advance beyond the stage of handcrafting domain specific features. Specifically, deep neural models incorporate varying levels of features to learn whether a document answers the information need of a query. However, these neural models rely on a large number of parameters to successfully learn a relation between a query and a relevant document.
This reliance on a large number of parameters, combined with the current methods of optimization relying on small updates necessitates numerous samples to allow the neural model to converge on an effective relevance function. This …