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..
Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition
Authors: Ben Adlam, Jeffrey Pennington
NeurIPS 2020 | Venue PDF | 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 EMAIL |
| 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. |