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..
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. |