Learning-Augmented Algorithms for the Bahncard Problem
Authors: Hailiang Zhao, Xueyan Tang, Peng Chen, Shuiguang Deng
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to compare PFSUM with SUM [1], PDLA [12], SUMw (Section 4.1), FSUM (Section 4.2), and SRL (Ski-Rental-based Learning algorithm), where SRL is adapted from Algorithm 2 proposed in [4] for ski-rental. |
| Researcher Affiliation | Academia | Hailiang Zhao1 Xueyan Tang2 Peng Chen1 Shuiguang Deng1 1Zhejiang University 2Nanyang Technological University {hliangzhao,pgchen,dengsg}@zju.edu.cn asxytang@ntu.edu.sg |
| Pseudocode | No | The paper describes algorithms (SUM, FSUM, PFSUM) in narrative text within the main body, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We have released the code and data for reproducibility. With our instructions, others can easily reproduce experimental results that are consistent with the results in the paper. (NeurIPS Checklist, Question 5) |
| Open Datasets | Yes | Input instances. Referring to the experimental setup of [31], we consider two main types of traveler profiles: commuters, and occasional travelers. We set the timespan at 2000 days... For each request sequence generated, we investigate three types of ticket price distributions: a Normal distribution... a Uniform distribution... and a Pareto distribution... The code and data can be openly accessed. (NeurIPS Checklist, Question 5) |
| Dataset Splits | No | The paper describes generating multiple input instances and running online algorithms on them; however, it does not specify explicit training, validation, or test dataset splits for model training or evaluation, as the algorithms are not traditional machine learning models requiring such splits. |
| Hardware Specification | No | The paper does not explicitly provide details about the specific hardware (e.g., GPU/CPU models, memory) used for running the experiments within the main text or appendices. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with their version numbers (e.g., Python, PyTorch, CUDA versions) required to replicate the experiments. |
| Experiment Setup | Yes | We test SUMw with w = T/2, and test SRL and PDLA with λ set to 0.2, 0.5, and 1. To accommodate SRL and PDLA, we discretize time over a sufficiently long timespan, closely approximating a continuous time scenario. |