
Asymptotics of Ridge Regression in Convolutional Models
Understanding generalization and estimation error of estimators for simp...
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Implicit Bias of Linear RNNs
Contemporary wisdom based on empirical studies suggests that standard re...
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LowRank Nonlinear Decoding of μECoG from the Primary Auditory Cortex
This paper considers the problem of neural decoding from parallel neural...
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Generalization Error of Generalized Linear Models in High Dimensions
At the heart of machine learning lies the question of generalizability o...
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Inference in MultiLayer Networks with MatrixValued Unknowns
We consider the problem of inferring the input and hidden variables of a...
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Inference with Deep Generative Priors in High Dimensions
Deep generative priors offer powerful models for complexstructured data...
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HighDimensional Bernoulli Autoregressive Process with LongRange Dependence
We consider the problem of estimating the parameters of a multivariate B...
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Asymptotics of MAP Inference in Deep Networks
Deep generative priors are a powerful tool for reconstruction problems w...
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Bilinear Recovery using Adaptive VectorAMP
We consider the problem of jointly recovering the vector b and the matri...
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Plugin Estimation in HighDimensional Linear Inverse Problems: A Rigorous Analysis
Estimating a vector x from noisy linear measurements Ax+w often requires...
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Inference in Deep Networks in High Dimensions
Deep generative networks provide a powerful tool for modeling complex da...
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Learning and Free Energies for Vector Approximate Message Passing
Vector approximate message passing (VAMP) is a computationally simple ap...
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Expectation Consistent Approximate Inference: Generalizations and Convergence
Approximations of loopy belief propagation, including expectation propag...
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Alyson K. Fletcher
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