Generalization Guarantee of SGD for Pairwise Learning
Authors: Yunwen Lei, Mingrui Liu, Yiming Ying
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the appendix, we present all the proofs, specific examples of pairwise learning and preliminary experimental results. In Section L we present preliminary experimental results to verify our stability bounds. |
| Researcher Affiliation | Academia | 1School of Computer Science, University of Birmingham, Birmingham B15 2TT, United Kingdom 2Department of Computer Science, George Mason University, Fairfax, VA 22030, USA 3Department of Mathematics and Statistics, State University of New York at Albany, USA |
| Pseudocode | Yes | We describe SGD(S, T, f, {ηt}) in Algorithm 1 of SGD with dataset S, iteration number T, loss function f and stepsize {ηt}. Algorithm 1: SGD(S, T, f, {ηt}) and Algorithm 2: Iterative Localized Algorithm for Pairwise Learning |
| Open Source Code | No | The paper does not provide any concrete access information (specific repository link, explicit code release statement, or code in supplementary materials) for the source code of the methodology described. |
| Open Datasets | No | The paper mentions 'preliminary experimental results' but does not provide concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |