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cs.LG2026
Dimension-Free Rank Lifting from Random Hyperplane Arrangements
Luca Becchetti, Matteo Russo, Ruben Skorupinski
We study the width required for a randomly initialized hidden layer of a neural network to achieve rank lifting. Namely, given a dataset of , …
cs.LG2026
Online Convex Optimization with Sublinear Noisy Probes
Simone Di Gregorio, Anupam Gupta, Stefano Leonardi +1
We study Online Convex Optimization (OCO) over a convex set , where in each round the learner selects and then observes a convex loss $f_t:K\…