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SUMMARY:ML-Opt: Romain Camilleri and Swati Padmanabhan
DESCRIPTION:The final talks of the ML-OPT seminar of the spring quarter will be given Friday (6/4) at 1:30pm PST by Romain Camilleri and Swati Padmanabhan.\n\n\nTitle: High-Dimensional Experimental Design and Kernel Bandits\n\nAbstract: I will talk about high-dimensional bandits. First I want to review how the classical approach to solving linear bandits motivates an experimental design problem. Then I plan to justify why common rounding techniques cannot be applied in a potentially infinite-dimensional space. Lastly\, I will show that one can avoid relying on rounding techniques by using a Catoni estimator.\n\nBio: Romain Camilleri is a 3rd year Ph.D. student at the Paul G. Allen School of Computer Science and Engineering at the University of Washington\, where he is advised by Kevin Jamieson.\n\n—————————————-\nTitle: Computing Lewis Weights to High Precision\n\nAbstract: We present an algorithm for computing high-precision approximate L_p Lewis weights for p > 2. Given an m x n real full-rank matrix A and p>=3\, our algorithm computes epsilon-approximate L_p-Lewis weights using  O(p^3 \log (m p / epsilon)) iterations\, where each iteration takes time linear in the sparsity of the input matrix plus the time to compute the leverage scores of a diagonal rescaling of A. Previously\, such iteration complexities were known only for 0< p < 4   [CohenPeng2015]. Consequently\, our result helps complete the picture on near-optimal reduction from leverage scores to L_p-Lewis weights for all p>0.\n[Joint work with Maryam Fazel\, Yin Tat Lee\, and Aaron Sidford]\n\n\nBio: Swati is a graduate student working on problems in convex optimization\, advised by Yin Tat Lee.
URL:https://ifds.info/event/ml-opt-romain-camilleri-and-swati-padmanabhan/
CATEGORIES:MLOpt@UWash
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