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Abstract: Existing research on polarization on social media platforms emphasizes the role of algorithmic "filter bubbles" and platform failure in amplifying extreme attitudes among online audiences. This article provides a […]
Abstract: Multi-group agnostic learning is a formal learning criterion that is concerned with the conditional risks of predictors within subgroups of a population. The criterion addresses recent practical concerns such as subgroup fairness and hidden stratification. I'll talk about the structure of solutions to the multi-group learning problem, as well as some simple and near-optimal algorithms for the learning problem. This is based on joint work with Christopher Tosh.
The Gaussian mixture block model is a simple generative model for networks: to generate a sample, we associate each node with a latent feature vector sampled from a mixture of […]
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.
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. […]
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 […]
On June 5th, 2023 the Halıcıoğlu Data Science Institute celebrated the 5-year anniversary since becoming its own academic department. In parallel to these celebrations, HDSI unveiled it's first dedication space, the Halıcıoğlu Data Science Institute building.
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.
Latent variable models have been an integral part of probabilistic machine learning, ranging from simple mixture models to variational autoencoders to powerful diffusion probabilistic models at the center of recent media attention. Perhaps less well-appreciated is the intimate connection between latent variable models and data compression, and the potential of these models for advancing natural science. This talk will explore these topics.