Debiasing Conditional Stochastic Optimization
Authors: Lie He, Shiva Kasiviswanathan
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we demonstrate the numerical performance of our proposed algorithms. We focus on the application of invariant logistic regression here. In Appendix F, we discuss performance of our proposed algorithms on other common CSO/FCCO applications. |
| Researcher Affiliation | Collaboration | Lie He EPFL lie.he@epfl.ch Shiva Prasad Kasiviswanathan Amazon kasivisw@gmail.com |
| Pseudocode | Yes | Algorithm 1 E-Nested VR; Algorithm 2 E-BSGD; Algorithm 3 E-BSpider Boost |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes generating synthetic datasets for experiments (e.g., "We generate a synthetic dataset with d = 10 dimensions" in Section 5.1 and "We generate the data similar to [31]" in Section 5.2) but does not mention using or providing access to any publicly available or open datasets. |
| Dataset Splits | No | The paper describes the data generation process for its experiments but does not provide specific training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory specifications, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | In each pair of experiments, the samples used per iteration are fixed same, i.e.: 1) BSGD uses m = 2 and E-BSGD uses m = 1; 2) For BSpider Boost and E-BSpider Boost, we use cycle length of 10, small batch and large batch in Spider to be 10 and 100 respectively, and we choose inner batch sizes m = 2 for BSpider Boost and m = 1 for E-BSpider Boost; 3) For Nested VR and E-Nested VR, we fix the outer batch size to 10 and 5 respectively, and choose fix the inner Spider Cycle to be 10 with large batch 100 and small batch 10. |