2.5k citations · 10k across the 171 of their papers we have counts for
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The generalization error of max-margin linear classifiers: Benign overfitting and high dimensional asymptotics in the overparametrized regime
Andrea Montanari, Feng Ruan, Youngtak Sohn +1
Modern machine learning classifiers often exhibit vanishing classification error on the training set. They achieve this by learning nonlinear representations of the inputs that map…
The generalization error of random features regression: Precise asymptotics and double descent curve
Song Mei, Andrea Montanari
Deep learning methods operate in regimes that defy the traditional statistical mindset. Neural network architectures often contain more parameters than training samples, and are so…
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_…
On the computational tractability of statistical estimation on amenable graphs
Ahmed El Alaoui, Andrea Montanari
We consider the problem of estimating a vector of discrete variables , based on noisy observations of the pairs on the edges of a graph $G=([…
Fundamental Barriers to High-Dimensional Regression with Convex Penalties
Michael Celentano, Andrea Montanari
In high-dimensional regression, we attempt to estimate a parameter vector from observations where …
Surprises in High-Dimensional Ridgeless Least Squares Interpolation
Trevor Hastie, Andrea Montanari, Saharon Rosset +1
Interpolators -- estimators that achieve zero training error -- have attracted growing attention in machine learning, mainly because state-of-the art neural networks appear to be m…
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…
Analysis of a Two-Layer Neural Network via Displacement Convexity
Adel Javanmard, Marco Mondelli, Andrea Montanari
Fitting a function by using linear combinations of a large number of `simple' components is one of the most fruitful ideas in statistical learning. This idea lies at the core o…