Online Learning of Delayed Choices
Authors: Recep Yusuf Bekci
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments that confirm the effectiveness of our algorithm. 7 Experiments: We conducted two sets of experiments to evaluate the performance of our algorithms. |
| Researcher Affiliation | Academia | Recep Yusuf Bekci University of Waterloo Waterloo, Canada recep.bekci@uwaterloo.ca |
| Pseudocode | Yes | Algorithm 1 Delayed MNL Bandit (DEMBA) |
| Open Source Code | Yes | The necessary information is provided in the Experiments section and in the appendix as well as the scripts for experiments are provided. |
| Open Datasets | No | The paper uses a synthetic dataset generated based on defined parameters (N=10, K=4, pi=1) and attraction parameters, rather than an existing public dataset. No access information is provided for this generated data. |
| Dataset Splits | No | The paper uses a simulation-based approach with synthetic data and measures cumulative regret over rounds, thus standard training, validation, and test dataset splits are not applicable or mentioned. |
| Hardware Specification | Yes | The simulations were conducted on a server equipped with 4 Intel Xeon 6248 2.5GHz CPUs and 377 GB of RAM, running Cent OS 7. |
| Software Dependencies | Yes | The simulation code was developed in Python version 3.9.6. |
| Experiment Setup | Yes | We used N = 10, K = 4 and pi = 1 for all i {1, . . . , N}. The attraction parameters were set as: vi = 0.25 + ϵ if i {1, 2, 9, 10} 0.25 otherwise, where ϵ represents the contrast between products. We used geometric delays with E[ds] = 100 and µ = 100 for the first experiment and E[ds] = 100 for the second experiment. |