Notice: 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. Full accuracy metrics and methodology are described in [1].
Online Resource Allocation with Non-Stationary Customers
Authors: Xiaoyue Zhang, Hanzhang Qin, Mabel Chou
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct extensive numerical experiments to show our approach generates near-optimal revenues for all different customer scenarios. |
| Researcher Affiliation | Academia | Institute of Operations Research and Analytics, National University of Singapore, Singapore 117602. |
| Pseudocode | Yes | We propose the ULwE algorithm, displayed in Algorithm 1, which is applicable in both stochastic and adversarial environments. |
| Open Source Code | No | The paper does not include any statement about releasing source code for the methodology or a link to a code repository. |
| Open Datasets | No | The experiments use simulated data rather than a publicly available dataset with a provided link or citation. The paper states: "Initially, based on the true rate ยต, we generate data for 500 customers to estimate the initial ฮปl values". |
| Dataset Splits | No | The paper mentions generating data for experiments but does not specify any training, validation, or test dataset splits. It describes how initial values are estimated and how `ฮปls` are updated, but not data partitioning for model training/evaluation. |
| Hardware Specification | No | The paper describes the experimental setup but does not provide specific details about the hardware used (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers used for implementing or running the experiments. |
| Experiment Setup | Yes | The experiments involve two customer types (L = 2) and two resources (n = 2), with customer purchase probabilities modeled through a logistic function...Customer Type A exhibits a high purchase probability of 0.9, while Customer Type B maintains a consistent probability of 0.5...The total capacity of both resources matches the time horizon T, with revenues set at 1 and 1.5 units for Resource 1 and 2, respectively. Our analysis primarily assesses the ULwE algorithm s performance, particularly its regret in both i.i.d. and adversarial customer arrival scenarios, highlighting its adaptability across various operational contexts. Experiment s regret values represent the average outcomes of 100 independent experiments, ensuring robustness and reliability of the results. |