Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Asymptotics of Ridge Regression in Convolutional Models
Authors: Mojtaba Sahraee-Ardakan, Tung Mai, Anup Rao, Ryan A. Rossi, Sundeep Rangan, Alyson K Fletcher
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we validate our theoretical results on simulated data. We generate data using a ground truth convolutional model of the form (3). We use i.i.d. complex normal convolution kernel and noise with different variances. For the data matrix X, we consider two different models: i) i.i.d. complex normal data; and ii) a non-Gaussian autoregressive process of order 1 (an AR(1) process). In both cases we take T = 256, ny = 500 and use different values of nx to create plots of estimation error with respect to δ = ny/nx. |
| Researcher Affiliation | Collaboration | 1Department of Electrical and Computer Engineering, University of California, Los Angeles 2Department of Statistics, University of California, Los Angeles 3Adobe Research 4Department of Electrical and Computer Engineering, New York University. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | The paper states: |
| Dataset Splits | No | The paper validates its theoretical results through simulations. It does not mention using standard training/validation/test splits of a publicly available dataset, nor does it define such splits for its simulated data. It describes parameters for generating data for comparison with theoretical predictions. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | We generate data using a ground truth convolutional model of the form (3). We use i.i.d. complex normal convolution kernel and noise with different variances. For the data matrix X, we consider two different models: i) i.i.d. complex normal data; and ii) a non-Gaussian autoregressive process of order 1 (an AR(1) process). In both cases we take T = 256, ny = 500 and use different values of nx to create plots of estimation error with respect to δ = ny/nx. ... The parameter a controls how fast the process is mixing. ... To show this, we use both a Gaussian AR(1) process with var(ξ2 t ) = 0.1 as well as ξt unif({ s, s}) with s = 0.1 to match the variances. In both cases we take a = 0.9 and measurement noise variance σ2 = 0.1. ... In this case the variance of signal and noise are 0.004 and 1 respectively. Figure 1 shows the log of normalized estimation error with respect to δ = ny/nx for three different values of the regularization parameter λ. |