Model Selection for Contextual Bandits
Authors: Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Additionally, Appendix A contains a validation experiment, where we demonstrate that Mod CB compares favorably to Lin UCB in simulations. |
| Researcher Affiliation | Collaboration | Dylan J. Foster Massachusetts Institute of Technology dylanf@mit.edu Akshay Krishnamurthy Microsoft Research NYC akshay@cs.umass.edu Haipeng Luo University of Southern California haipengl@usc.edu |
| Pseudocode | Yes | Algorithm 1 Mod CB (Model Selection for Contextual Bandits) ... Algorithm 2 Estimate Residual |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and refers to a 'validation experiment' in Appendix A but does not mention specific public datasets, nor does it provide any concrete access information (link, citation) for any dataset used. |
| Dataset Splits | No | The paper mentions a 'validation experiment' but does not provide specific details about training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python, PyTorch, specific solvers). |
| Experiment Setup | No | The paper is highly theoretical and describes algorithm parameters (e.g., C1, C2, µt, δ0) that are used in its theoretical guarantees, but it does not provide specific hyperparameter values or training configurations for a practical experiment setup in the main text. |