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Found 3 papers, 1 papers with code
Dimension-free deterministic equivalents for random feature regression
Our main contribution is a general deterministic equivalent for the test error of RFRR.
Fundamental limits of weak learnability in high-dimensional multi-index models
Multi-index models -- functions which only depend on the covariates through a non-linear transformation of their projection on a subspace -- are a useful benchmark for investigating feature learning with neural networks.
High-dimensional robust regression under heavy-tailed data: Asymptotics and Universality
In particular, we provide a sharp asymptotic characterisation of M-estimators trained on a family of elliptical covariate and noise data distributions including cases where second and higher moments do not exist.
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