Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition
Authors: Ben Adlam, Jeffrey Pennington
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Figure 2: Comparison of (a) ensembles and (b) bagging. Solid lines are theoretical predictions and dots are simulation results. |
| Researcher Affiliation | Industry | Ben Adlam Jeffrey Pennington* Google Brain {adlam, jpennin}@google.com |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing code or links to a code repository for the methodology described. |
| Open Datasets | No | The paper describes a synthetic data generation process for its analysis: 'We consider the task of learning an unknown function from m independent samples (xi, yi) 2 Rn0 R, i = 1, . . . , m, where the datapoints are standard Gaussian, xi N(0, In0), and the labels are generated by a linear function parameterized by β 2 Rn0, whose entries are drawn independently from N(0, 1).' |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, or predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running its experiments or simulations. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | In (a,b) we set γ = 10 6, n0 = 213, m = 214, σ = tanh, and SNR = 5. |