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cs.LG2026
Optimal Dimension-Free Sampling for Regularized Classification
Meysam Alishahi, Alexander Munteanu, Simon Omlor +1
We prove optimal sampling bounds achieving -relative error for a broad class of Lipschitz continuous classification loss functions under various regularization t…
cs.LG2026
TabKDE: Simple and Scalable Tabular Data Generation with Kernel Density Estimates
Meysam Alishahi, Yan Zheng, Junpeng Wang +2
Tabular data generation considers a large table with multiple columns -- each column comprised of numerical, categorical, or sometimes ordinal values. The goal is to produce new ro…
cs.LG2024
No Dimensional Sampling Coresets for Classification
Meysam Alishahi, Jeff M. Phillips
We refine and generalize what is known about coresets for classification problems via the sensitivity sampling framework. Such coresets seek the smallest possible subsets of input…