Second-Order Information and Applications

Kristof Schröder will talk about second-order information and its applications in machine learning.


Second-order information, primarily represented through Hessians or curvature matrices, offers profound insights beyond mere gradient information. Incorporating this kind of information typically results in a substantially higher computational cost. To alleviate this, common strategies involve utilizing low-rank approximations or introducing elements of randomness to manage complexity. In this presentation, we will explore recent applications in the field that highlight the utility and scalability of second-order algorithms.


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