Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Generalized Leverage Score Sampling for Neural Networks
Authors: Jason D. Lee, Ruoqi Shen, Zhao Song, Mengdi Wang, zheng Yu
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 18,336 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,225 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We summarize our main results and contribution as following: Generalize the leverage score sampling theory for kernel ridge regression to a broader class of kernels. Connect the leverage score sampling theory with neural network training. Theoretically prove the equivalence between training regularized neural network and kernel ridge regression under both random Gaussian initialization and leverage score sampling initialization. The focus of this paper is purely theoretical, and thus a broader impact discussion is not applicable. |
| Researcher Affiliation | Academia | EMAIL Princeton University. Work done while visiting Institute for Advanced Study. EMAIL University of Washington. Work done while visiting Institute for Advanced Study. EMAIL Columbia University, Princeton University and Institute for Advanced Study. EMAIL Princeton University. EMAIL Princeton University. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states 'The full version of this paper is available at https://arxiv.org/pdf/2009.09829.pdf' which is a link to the paper itself, not to source code. No other statement about code availability is present. |
| Open Datasets | No | This paper focuses on theoretical analysis and does not describe experiments involving datasets. |
| Dataset Splits | No | This paper is theoretical and does not describe experiments with dataset splits. |
| Hardware Specification | No | This is a theoretical paper and does not report on experiments or hardware specifications. |
| Software Dependencies | No | This paper is theoretical and does not describe software dependencies or specific versions. |
| Experiment Setup | No | This paper is theoretical and does not include details about an experimental setup or hyperparameters. |