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learning algorithms
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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: Jinchao Xu (Kaust), “Finite Element versus Finite Neuron Methods”
Talk summary: This talk presents a unified framework connecting Barron and Sobolev spaces to analyze the approximation properties of ReLU$^k$ neural networks. It establishes […]
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FDS Colloquium: Jun’ichi Takeuchi (Kyushu), “Fisher information and Neural Tangent Kernels”
Abstract: We argue relation between neural tangent kernels (NTK) and Fisher information matrices of neural networks. For the Fisher information matrices of two layer […]
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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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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 […]
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S&DS Colloquium: Thuy-Duong “June” Vuong (Miller Institute, Berkeley), “Efficiently learning and sampling from multimodal distributions using data-based initialization”
Abstract: Learning to sample is a central task in generative AI: the goal is to generate (infinitely many more) samples from a target distribution $\mu$ […]