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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Bandits with Knapsacks: Advice on Time-Varying Demands
Authors: Lixing Lyu, Wang Chi Cheung
ICML 2023 | Venue PDF | 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. |