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..
Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces
Authors: Yinglun Zhu, Paul Mineiro
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct large-scale empirical evaluations demonstrating the efficacy of our proposed algorithms. |
| Researcher Affiliation | Collaboration | Yinglun Zhu 1 Paul Mineiro 2 1University of Wisconsin-Madison 2Microsoft Research NYC. |
| Pseudocode | Yes | Algorithm 1 Smooth IGW. Algorithm 2 Rejection Sampling for IGW. Algorithm 3 Stable Base Algorithm (Index b). |
| Open Source Code | Yes | Code to reproduce these experiments is available at https://github.com/pmineiro/smoothcb. |
| Open Datasets | Yes | We replicate the real-world dataset experiment from Zhu & Nowak (2020). We replicate the online setting from Majzoubi et al. (2020), where 5 large-scale Open ML regression datasets are converted into continuous action problems. |
| Dataset Splits | No | The paper mentions using datasets but does not explicitly specify the training, validation, and test splits (e.g., percentages or counts) required for reproduction in the main text. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for its experiments (e.g., specific GPU or CPU models). |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | No | The paper mentions hyperparameter choices were made (e.g., "initial hyperparameter choices", "tune hyperparameters") but does not provide the concrete values of these hyperparameters or other system-level training settings in the main text. |