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Representation Learning: A Causal Perspective
Representation Learning: A Causal Perspective
Abstract: Representation learning constructs low-dimensional representations to summarize essential features of high-dimensional data like images and texts. Ideally, such a representation should efficiently capture non-spurious features of the data. It shall also be disentangled so that we can interpret what feature each of its dimensions captures. However, these desiderata are often intuitively defined and challenging to quantify or enforce.
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On the complexity of Frank-Wolfe methods
On the complexity of Frank-Wolfe methods
Abstract: Frank-Wolfe methods are popular for optimization over a polytope. One of the reasons is because they do not need projection onto the polytope but only linear optimization over it. […]
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HDSI 2023 Undergraduate Scholarship Showcase
HDSI 2023 Undergraduate Scholarship Showcase
The Halıcıoğlu Data Science Institute is preparing to host our annual Undergraduate Scholarship Showcase. We invite you to join our scholarship cohort in an interactive presentation of the projects they have […]
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Samuel Lau | Instructor-Centered Design of Tools to Support Teaching Programming and Data Science At Scale
Samuel Lau | Instructor-Centered Design of Tools to Support Teaching Programming and Data Science At Scale
Instructors of technical subjects like programming and data science use a wide array of software tools that enable them to create sophisticated and engaging lessons at scale. Although there are many such tools available, instructors often find themselves repurposing software originally designed for other people, like professional software engineers. To address these issues, this dissertation takes an instructor-centered approach. It surfaces previously unmet needs through studies of instructors, their goals, and their software tools.