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07/28/2023
On Single Index Models beyond Gaussian Data
Sparse high-dimensional functions have arisen as a rich framework to stu...
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02/13/2023
Kernelized Diffusion maps
Spectral clustering and diffusion maps are celebrated dimensionality red...
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10/11/2022
SGD with large step sizes learns sparse features
We showcase important features of the dynamics of the Stochastic Gradien...
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06/20/2022
Label noise (stochastic) gradient descent implicitly solves the Lasso for quadratic parametrisation
Understanding the implicit bias of training algorithms is of crucial imp...
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06/02/2022
Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs
The training of neural networks by gradient descent methods is a corners...
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06/17/2021
Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity
Understanding the implicit bias of training algorithms is of crucial imp...
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02/05/2021
Last iterate convergence of SGD for Least-Squares in the Interpolation regime
Motivated by the recent successes of neural networks that have the abili...
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05/25/2018
Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes
We consider stochastic gradient descent (SGD) for least-squares regressi...
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12/13/2017