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Hannah Lawrence
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2026-02-14 03:32:09
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GULP: a prediction-based metric between representations Enric Boix-Adsera , Hannah Lawrence , George Stepaniants , Philippe Rigollet NeurIPS (Oral Presentation) , 2022 We define a family of distance pseudometrics for comparing learned data representations, directly inspired by transfer learning. In particular, we define a distance between two representations based on how differently (worst-case over all downstream, bounded linear predictive tasks) they perform under ridge regression. Barron's Theorem for Equivariant Networks Hannah Lawrence NeurIPS Workshop: Symmetry and Geometry in Neural Representations (Poster, to appear) , 2022 We extend Barron’s Theorem for efficient approximation to invariant neural networks, in the cases of invariance to a permutation subgroup or the rotation group. Toeplitz Low-Rank Approximation with Sublinear Query Complexity Michael Kapralov , Hannah Lawrence , Mikhail Makarov , Cameron Musco , Kshiteej Sheth Symposium o...
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