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..
EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits
Authors: Yikun Ban, Yuchen Yan, Arindam Banerjee, Jingrui He
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on four real-world datasets, showing that EE-Net outperforms existing linear and neural contextual bandit baselines on real-world datasets. |
| Researcher Affiliation | Academia | Yikun Ban, Yuchen Yan, Arindam Banerjee, Jingrui He University of Illinois at Urbana-Champaign EMAIL |
| Pseudocode | Yes | Algorithm 1 EE-Net |
| Open Source Code | Yes | Codes are available at 1. 1https://github.com/banyikun/EE-Net-ICLR-2022 |
| Open Datasets | Yes | We use four real-world datasets: Mnist, Yelp, Movielens, and Disin, the details and settings of which are attached in Appendix A. ... MNIST dataset. MNIST is a well-known image dataset (Le Cun et al., 1998)... Yelp and Movielens (Harper and Konstan, 2015) datasets. ... Disin (Ahmed et al., 2018) dataset. |
| Dataset Splits | No | The paper describes an online learning bandit problem where data is processed sequentially over T rounds. It does not provide explicit training, validation, and test splits with percentages or counts, as is typical for static supervised learning datasets. |
| Hardware Specification | No | The paper does not specify the hardware used for experiments, such as particular CPU or GPU models, or cloud computing instances with their specifications. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., Python, PyTorch versions, or other libraries). |
| Experiment Setup | Yes | For all neural networks, we conduct the grid search for learning rate over (0.01, 0.001, 0.0005, 0.0001). For all grid-searched parameters, we choose the best of them for the comparison and report the averaged results of 10 runs for all methods. ... For all the neural-based methods including EE-Net, the exploitation network f1 is built by a 2-layer fully-connected network with 100 width. For the exploration network f2, we use a 2-layer fully-connected network with 100 width as well. |