How Data Augmentation affects Optimization for Linear Regression
Authors: Boris Hanin, Yi Sun
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate Theorems 4.1 and 4.2, we ran augmented GD and SGD with additive Gaussian noise on N = 100 simulated datapoints. ... Figure 4.1 shows MSE and Wt, F along a single optimization trajectory with different schedules for the variance σ2 t used in Gaussian noise augmentation. |
| Researcher Affiliation | Academia | Boris Hanin Department of Operations Research and Financial Engineering Princeton University bhanin@princeton.edu; Yi Sun Department of Statistics University of Chicago yisun@statistics.uchicago.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Complete code to generate this figure is provided in supplement.zip in the supplement. |
| Open Datasets | No | The paper states 'N = 100 simulated datapoints' and 'Inputs were i.i.d. Gaussian vectors in dimension n = 400', indicating the data was generated for the experiments rather than being a publicly accessible dataset with concrete access information. |
| Dataset Splits | No | The paper mentions running experiments on 'simulated datapoints' but does not provide specific details about training, validation, or test dataset splits, percentages, or sample counts. |
| Hardware Specification | No | The paper mentions 'It ran in 30 minutes on a standard laptop CPU.' This is a general statement and does not provide specific hardware details such as CPU model, GPU models, or memory. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments (e.g., Python, PyTorch, TensorFlow, etc.). |
| Experiment Setup | Yes | The learning rate followed a fixed polynomially decaying schedule ηt = 0.005 / (100 * (batch size)) / (1 + t / 20)^0.66, and the batch size used for SGD was 20. |