Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 4 of 4 for “"Fairness in Machine Learning"”.
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Achieving Differential Privacy and Fairness in Machine Learning
<p>Machine learning algorithms are used to make decisions in various applications, such as recruiting, lending and policing. These algorithms rely on large amounts of sensitive individual information to work properly. Hence, there are sociological concerns about machine learning algorithms on …
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Improving Efficiency and Fairness in Machine Learning: a Discrete Optimization Approach
In recent years, machine learning models are being increasingly deployed in various applications including Education, Finance, Healthcare, Transportation, etc. However, in most practical situations one-size-fits-all solutions suffer from poor predictive performance and/or bias against certain …
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Scalable, Efficient, and Fair Algorithms for Structured Convex Optimization Problems
The growth of machine learning and data science has necessitated the development of provably fast and scalable algorithms that incorporate ethical requirements. In this thesis, we present algorithms for fundamental optimization algorithms with theoretical guarantees on approximation quality and …
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Learning speech embeddings for speaker adaptation and speech understanding
In recent years, deep neural network models gained popularity as a modeling approach for many speech processing tasks including automatic speech recognition (ASR) and spoken language understanding (SLU). In this dissertation, there are two main goals. The first goal is to propose modeling …