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Found 4 papers, 1 papers with code
On the Growth of Mistakes in Differentially Private Online Learning: A Lower Bound Perspective
In this paper, we provide lower bounds for Differentially Private (DP) Online Learning algorithms.
Asymptotics of Learning with Deep Structured (Random) Features
For a large class of feature maps we provide a tight asymptotic characterisation of the test error associated with learning the readout layer, in the high-dimensional limit where the input dimension, hidden layer widths, and number of training samples are proportionally large.
Deterministic equivalent and error universality of deep random features learning
Establishing this result requires proving a deterministic equivalent for traces of the deep random features sample covariance matrices which can be of independent interest.
