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.
SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk
Authors: Guillaume Papa, Stéphan Clémençon, Aurélien Bellet
NeurIPS 2015 | Venue PDF | LLM Run Details | Input Tokens: 14,621 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,672 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Beyond the rate bound analysis, experiments on AUC maximization and metric learning provide strong empirical evidence of the superiority of the proposed approach. ... In this section, we provide numerical experiments to compare the incomplete and complete U-statistic gradient estimators (5) and (6) in SGD when they rely on the same number of terms B. |
| Researcher Affiliation | Academia | Guillaume Papa, St ephan Cl emenc on LTCI, CNRS, T el ecom Paris Tech Universit e Paris-Saclay, 75013 Paris, France ... Aur elien Bellet Magnet Team, INRIA Lille Nord Europe 59650 Villeneuve d Ascq, France |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide explicit concrete access to source code for the methodology described. |
| Open Datasets | Yes | The datasets we use are available online.1 (footnote 1 points to http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/) |
| Dataset Splits | No | The paper states: 'In all experiments, we randomly split the data into 80% training set and 20% test set'. It does not explicitly mention a validation set split. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with versions) needed to replicate the experiment. |
| Experiment Setup | Yes | We used a step size of the form γt = γ1/t, and the results below are with respect to the number of SGD iterations. ... We try different values for the initial step size γ1 and the batch size B. |