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Machine Learning
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Dissertation Defense: Anay Mehrotra, “Learning Theory in the Wild: Foundations of Missing Data and Language Generation”
Abstract: What can be learned from data? This fundamental question in machine learning takes on new complexity in modern pipelines where classical assumptions fail—both […]
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FDS Colloquium: Lorenzo Orecchia (Chicago), “Variational Characterizations of First-Order Algorithms via Self-Duality”
Talk summary: First-order methods for convex optimization play an important role in the efficient deployment of machine learning algorithms. While a large number of […]
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FDS Colloquium: George Lan (Georgia Tech), “Algorithmic Foundations of Risk-averse Optimization for Trustworthy AI”
Talk summary: Over the past two decades, stochastic optimization has made remarkable strides, driving its widespread adoption in machine learning (ML) and artificial intelligence (AI). […]
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FDS Seminar: Akhil Premkumar (UChicago), “An information theoretic view of machine learning”
Abstract: Diffusion models serve as a bridge between generative AI and information theory. These models have demonstrated a remarkable ability to learn high-dimensional continuous distributions, […]
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Yale Student Theory Day
Join us for a fun day of research talks presented by Yale CS and S&DS graduate students, additionally featuring invited speakers from universities in […]