Flat Minima in Linear Estimation and an Extended Gauss Markov Theorem
Authors: Simon Segert
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Additionally, we analytically derive the generalization error in multiple random matrix ensembles, and compare with Ridge regression. Finally, we conduct an extensive simulation study, in which we show that the cross-validated Nuclear and Spectral regressors can outperform Ridge in several circumstances. |
| Researcher Affiliation | Academia | Simon Segert Princeton Neuroscience Institute Princeton University ssegert@princeton.edu |
| Pseudocode | No | The paper describes mathematical derivations and experimental procedures in text but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about releasing its source code or links to a code repository. |
| Open Datasets | No | The paper describes generating synthetic datasets from 'spherical Gaussian ensemble' and 'diagonal ensemble' (Section 2.2), and using 'Random Fourier features' (Section 3.2.2). These are not named, publicly available datasets with specific access information (links, DOIs, etc.). |
| Dataset Splits | Yes | For each model (Spectral, Ridge, Nuclear) and each individual dataset, we used 3-fold cross validation to select the best-performing on the training set. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'sklearn.kernel_approximation.RBFSampler' but does not provide specific version numbers for this or any other software dependencies. |
| Experiment Setup | Yes | In all cases, we used N = 100 training examples, and 5000 testing examples. ... The set of allowable values considered in the cross-validation was the same for all models, and consisted of 9 equally logarithmically spaced values spanning 10^-4 to 10^6. |