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Professor Courtney Paquette from the Department of Mathematics and Statistics is featured in SIAM News鈥攖he journal of the Society for Industrial and Applied Mathematics (SIAM). The publication showcases the state of the art in applied mathematics, computational science, and data science, while highlighting real-world applications of mathematical research. In doing so, it helps lay the groundwork for scientific advances and new discoveries, supports efforts to address pressing global challenges, and enables leaders and policymakers to make informed decisions.

Courtney Paquette of 9I制作厂免费 and Google Research explored this topic during her听听at the听, which took place this summer in Montr茅al, Qu茅bec, Canada. Paquette鈥檚 talk focused on her efforts to predict the behavior of large models that are trained via stochastic gradient descent (SGD). While her work may be classified as optimization theory, there are important distinctions. Paquette argues that mathematicians in this area of research need to understand the way in which traditional optimization theory contrasts with practical ML. In practice, noise is everywhere; training data have sources of noise or measurement error, initialization for iterative schemes that optimize parameter sets听听usually start with randomly initialized weights听, and the iterations often involve random sampling of the training data (as with SGD).

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