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.
Contextual Conservative Interleaving Bandits
Authors: Kei Takemura
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 28,132 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,560 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 | Our numerical experiments on a real-world dataset demonstrate that GCW with the standard bandit algorithms efficiently improves performance while satisfying the performance constraints. |
| Researcher Affiliation | Industry | 1NEC Corporation, Tokyo, Japan. |
| Pseudocode | Yes | Algorithm 1 Greedy on confidence widths |
| Open Source Code | No | The paper does not contain an explicit statement about the release of source code or a link to a code repository. |
| Open Datasets | Yes | We use the Book-Crossing dataset (Ziegler et al., 2005), which consists of ratings expressed on a scale from 0 to 10 for books. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions various algorithms and their parameters (Table 2), but does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | In each setting, we set d = 20, T = 1000, k = 30, and n = 10. |