EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits
Authors: Yikun Ban, Yuchen Yan, Arindam Banerjee, Jingrui He
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | 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 {yikunb2, yucheny5, arindamb, jingrui}@illinois.edu |
| 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. |