91 citations · 179 across the 5 of their papers we have counts for
7 papers
Minimum complexity interpolation in random features models
Michael Celentano, Theodor Misiakiewicz, Andrea Montanari
Despite their many appealing properties, kernel methods are heavily affected by the curse of dimensionality. For instance, in the case of inner product kernels in , t…
Learning with invariances in random features and kernel models
Song Mei, Theodor Misiakiewicz, Andrea Montanari
A number of machine learning tasks entail a high degree of invariance: the data distribution does not change if we act on the data with a certain group of transformations. For inst…
Generalization error of random features and kernel methods: hypercontractivity and kernel matrix concentration
Song Mei, Theodor Misiakiewicz, Andrea Montanari
Consider the classical supervised learning problem: we are given data , , with a response and a covariate…
Limitations of Lazy Training of Two-layers Neural Networks
Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz +1
We study the supervised learning problem under either of the following two models: (1) Feature vectors are -dimensional Gaussians and responses are $y_i = f_…
Linearized two-layers neural networks in high dimension
Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz +1
We consider the problem of learning an unknown function on the -dimensional sphere with respect to the square loss, given i.i.d. samples $\{(y_i,{\boldsymbol x}_i)\}…
Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit
Song Mei, Theodor Misiakiewicz, Andrea Montanari
We consider learning two layer neural networks using stochastic gradient descent. The mean-field description of this learning dynamics approximates the evolution of the network wei…