Bandits with Knapsacks: Advice on Time-Varying Demands
Authors: Lixing Lyu, Wang Chi Cheung
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theoretical results are corroborated by our numerical findings. We perform numerical experiments when {qt}T t=1 is governed by a time series model. The experiment highlights the benefit of predicitons. We show that an online algorithm, such as OA-UCB, that harnesses predictions judiciously can perform empirically better than existing baselines, which only has access to the bandit feedback from the latent environment. |
| Researcher Affiliation | Academia | 1Institute of Operations Research and Analytics, National University of Singapore, Singapore 2Department of Industrial Systems Engineering and Management, National University of Singapore, Singapore. |
| Pseudocode | Yes | Algorithm 1 Online-advice-UCB (OA-UCB); Algorithm 2 Estimation Generation Policy |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | Demand sequence {qt}T t=1: We apply an AR(1) model to generate {qt}: qt = α + βqt−1 + εt, where ε1, . . . εT ∼ N(0, σ2) are independent. We simulate our algorithm and the benchmarks on a family of instances, with K = 10, d = 3, b = 3, α = 2, β = 0.5, σ = 0.5, and T varies from 5000 to 15000. |
| Dataset Splits | No | The paper describes generating synthetic data and simulating experiments over a horizon T, but it does not specify any training, validation, or test dataset splits in the conventional sense (e.g., percentages or counts of a fixed dataset). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as CPU/GPU models or memory specifications. |
| Software Dependencies | No | The paper mentions "time series prediction tools in Python, MatLab or R" but does not specify any software libraries or packages with version numbers needed to replicate the experiments. |
| Experiment Setup | Yes | In the experiment, we simulate our algorithm and the benchmarks on a family of instances, with K = 10, d = 3, b = 3, α = 2, β = 0.5, σ = 0.5, and T varies from 5000 to 15000. |