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..
Piecewise-Stationary Bandits with Knapsacks
Authors: Xilin Zhang, Wang Chi Cheung
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We run numerical experiments on a single-resource problem where L 2, T 20000 (each stationary piece has 10000 rounds), K t1, 2u, B 9360 and we set α e for our algorithms. The rewards and resource consumption in all rounds are uniformly distributed within a r 0.2, 0.2s range from their mean values. We compare the performance of IRES-CM with Immorlica et al. (2019) s algorithm and Zhou et al. (2008) s algorithm. Recall that Immorlica et al. (2019) focus on an adversarial Bwk problem and achieves a CR w.r.t. a static benchmark. Zhou et al. (2008) study a full-feedback adversarial setting and achieves a CR w.r.t. a single best arm benchmark. In Figure 1, each curve represents the average cumulative reward over 10 simulations, and the shaded area around each curve marks the variance over the simulations. |
| Researcher Affiliation | Academia | Xilin Zhang Department of ISEM National University of Singapore Singapore, 117578 EMAIL Cheung Wang Chi Department of ISEM National University of Singapore Singapore, 117578 EMAIL |
| Pseudocode | Yes | Algorithm 1 Inventory REServing with deterministic input (IRES) ... Algorithm 2 Inventory REServing with Change Monitoring (IRES-CM) |
| Open Source Code | No | Our data are numerically generated and codes can be provided. |
| Open Datasets | No | The paper generates its own data for numerical experiments: 'The rewards and resource consumption in all rounds are uniformly distributed within a r 0.2, 0.2s range from their mean values.' |
| Dataset Splits | No | The paper describes simulation parameters and runs, but does not specify a train/validation/test dataset split as it uses generated data in an online learning setting. |
| Hardware Specification | Yes | Finally, our experiments are run on a Surface Pro 7 with an i5-1035G4 processor. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We run numerical experiments on a single-resource problem where L 2, T 20000 (each stationary piece has 10000 rounds), K t1, 2u, B 9360 and we set α e for our algorithms. |