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09/05/2023
Maximum Mean Discrepancy Meets Neural Networks: The Radon-Kolmogorov-Smirnov Test
Maximum mean discrepancy (MMD) refers to a general class of nonparametri...
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09/04/2023
Challenges of the inconsistency regime: Novel debiasing methods for missing data models
We study semi-parametric estimation of the population mean when data is ...
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08/19/2022
Sudakov-Fernique post-AMP, and a new proof of the local convexity of the TAP free energy
In many problems in modern statistics and machine learning, it is often ...
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12/14/2021
The high-dimensional asymptotics of first order methods with random data
We study a class of deterministic flows in ℝ^d× k, parametrized by a ran...
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07/29/2021
CAD: Debiasing the Lasso with inaccurate covariate model
We consider the problem of estimating a low-dimensional parameter in hig...
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06/21/2021
Local convexity of the TAP free energy and AMP convergence for Z2-synchronization
We study mean-field variational Bayesian inference using the TAP approac...
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03/30/2021
Minimum complexity interpolation in random features models
Despite their many appealing properties, kernel methods are heavily affe...
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07/27/2020
The Lasso with general Gaussian designs with applications to hypothesis testing
The Lasso is a method for high-dimensional regression, which is now comm...
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02/28/2020
The estimation error of general first order methods
Modern large-scale statistical models require to estimate thousands to m...
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06/25/2019
Approximate separability of symmetrically penalized least squares in high dimensions: characterization and consequences
We show that the high-dimensional behavior of symmetrically penalized le...
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03/25/2019
