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Optimization
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Xiaohong Chen named the Koopmans Professor of Economics
Chen, who has made fundamental contributions to econometric methods, joined the Yale faculty in 2007. Xiaohong Chen, a renowned scholar who has made fundamental contributions to econometric methods, has been named the Tjalling C. Koopmans Professor of Economics, effective July 1. She is a member of the Department of Economics in Yale’s Faculty of Arts and Sciences…
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FDS Colloquium: John Duchi (Stanford), “Finding Stationary Points in Stochastic Convex Optimization: Why and How”
Abstract: In this talk, I will discuss finding stationary points of possibly non-differentiable stochastic convex functions. Motivation for solving such problems arises in modern “distribution free” statistical learning problems. We will highlight some of the challenges of solving such problems, including that subgradients of stochastic convex functions do not converge uniformly, and also demonstrate some…
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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 such methods exist, each tuned to the specific properties of the problem under consideration, it is not always clear how to generalize their approach to a new setting or to a…
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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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FDS Colloquium: Pravesh Kothari (Princeton), “The surprising reach of spectral algorithms for smoothed k-SAT”
Abstract: Semirandom input models are hybrids of the classical worst-case and average-case models in algorithm design. They were introduced in the 1990s to inspire “robust heuristics” that, on the one hand, escape worst-case hardness results, while, on the other, avoid “overfitting” to a specific distribution of input instances. Over the past five years, rapid progress…
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FDS Colloquium: Elliot Paquette (McGill), “High-dimensional Optimization with Applications to Compute-Optimal Neural Scaling Laws”
Abstract: Given the massive scale of modern ML models, we now only get a single shot to train them effectively. This restricts our ability to test multiple architectures and hyper-parameter configurations. Instead, we need to understand how these models scale, allowing us to experiment with smaller problems and then apply those insights to larger-scale models. In this talk,…