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 [1].
Misspecified Linear Bandits
Authors: Avishek Ghosh, Sayak Ray Chowdhury, Aditya Gopalan
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments on synthetic data, and on recommendation data from the public Yahoo! Learning to Rank Challenge dataset, empirically support our findings. |
| Researcher Affiliation | Academia | Avishek Ghosh University of California, Berkeley California 94720 USA Sayak Ray Chowdhury Indian Institute of Science Bengaluru 560012 India Aditya Gopalan Indian Institute of Science Bengaluru 560012 India |
| Pseudocode | Yes | Algorithm 1 Robust Linear Bandit (RLB) |
| Open Source Code | No | The paper does not provide an explicit statement or link to the authors' own source code for the described methodology. |
| Open Datasets | Yes | Numerical experiments on synthetic data, and on recommendation data from the public Yahoo! Learning to Rank Challenge dataset, empirically support our ๏ฌndings. (Footnote 3: https://webscope.sandbox.yahoo.com/catalog.php?datatype=c) |
| Dataset Splits | No | The paper uses the Yahoo! dataset but does not explicitly specify training, validation, or test splits with percentages or sample counts. It only mentions using 'set2.test.txt'. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not mention specific software names with version numbers, such as libraries or programming language versions. |
| Experiment Setup | Yes | In this setup, we assume, N = 1000, d = 20 and k = 50. ฮป and R are taken as 0.001 and 0.1 respectively. |