Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Linear Programming for Large-Scale Markov Decision Problems
Authors: Alan Malek, Yasin Abbasi-Yadkori, Peter Bartlett
ICML 2014 | Venue PDF | LLM Run Details | Input Tokens: 16,730 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,619 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Preliminary experiments show the effectiveness of the proposed algorithm in a queuing application. |
| Researcher Affiliation | Academia | Yasin Abbasi-Yadkori EMAIL Queensland University of Technology, Brisbane, QLD, Australia 4000 Peter L. Bartlett EMAIL University of California, Berkeley, CA 94720 and Queensland University of Technology, Brisbane, QLD, Australia 4000 Alan Malek EMAIL University of California, Berkeley, CA 94720 |
| Pseudocode | Yes | The algorithm is shown in Figure 1. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper applies its algorithm to a 'four-dimensional discrete-time queueing network' and specifies parameters for this network, but it does not mention a publicly available dataset, nor does it provide a link, DOI, or formal citation for data access. It describes a simulated environment rather than using an external public dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts) for training, validation, or testing for the simulated queueing network. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. It only mentions 'simulations'. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | We used a1 = a3 = .08, d1 = d2 = .12, and d3 = d4 = .28, and buffer sizes B1 = B4 = 38, B2 = B3 = 25 as the parameters of the network. ... our learning rate began at 10 4 and halved every 2000 iterations. |