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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 in how data is generated and in how learning objectives are defined. This thesis develops foundations for learning under these complex conditions, revealing how violations of traditional assumptions transform not just…
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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 both classical and new sharp approximation rates, showing that for functions in the relevant Barron space, ReLU$^k$ networks can achieve high accuracy without the curse of dimensionality. The same convergence rate…
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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 ReLU neural networks with random hidden weights, we demonstrated their approximate spectral decomposition, whose eigenvalue distribution highly concentrates (Takeishi et al. 2023). In particular, the sum of the top 3 eigenvalues…
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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). However, most existing models prioritize minimizing expected loss, often leaving AI-driven decisions vulnerable to costly or catastrophic failures and raising concerns about their trustworthiness in high-stakes applications. Risk-averse optimization provides a principled…
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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 the broader NY / New England area. This one-day workshop hopes to bring together the student community while showcasing some exciting work being done on theoretical aspects of computer science, algorithms…
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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 $\mu$ given a small number of samples from $\mu.$ It is well-known that traditional algorithms such as Glauber or Langevin dynamics are highly inefficient when the target distribution is multimodal, as they…