Near-Interpolators: Rapid Norm Growth and the Trade-Off between Interpolation and Generalization

We study the generalization capability of nearly-interpolating linear regressors: β's whose training error τ is positive but small, i.e., below the noise floor. Under a random matrix theoretic assumption on the data distribution and an eigendecay assumption on the data covariance matrix Σ, we demonstrate that any near-interpolator exhibits rapid norm growth: for τ fixed, β has squared ℓ_2-norm 𝔼[β_2^2] = Ω(n^α) where n is the number of samples and α >1 is the exponent of the eigendecay, i.e., λ_i(Σ) ∼ i^-α. This implies that existing data-independent norm-based bounds are necessarily loose. On the other hand, in the same regime we precisely characterize the asymptotic trade-off between interpolation and generalization. Our characterization reveals that larger norm scaling exponents α correspond to worse trade-offs between interpolation and generalization. We verify empirically that a similar phenomenon holds for nearly-interpolating shallow neural networks.