Synthetic Data

doi:doi:10.57702/ud58iifq

Synthetic Data

The dataset used in the paper is a synthetic dataset for off-policy contextual bandits, with contexts x ∈ X, a finite set of actions A, and bounded real rewards r ∈ A → [0, 1].

BibTex:

@dataset{Gemma_E_Moran_and_Dhanya_Sridhar_and_Yixin_Wang_and_David_M_Blei_2024,
    abstract = {The dataset used in the paper is a synthetic dataset for off-policy contextual bandits, with contexts x ∈ X, a finite set of actions A, and bounded real rewards r ∈ A → [0, 1].},
    author = {Gemma E. Moran and Dhanya Sridhar and Yixin Wang and David M. Blei},
    doi = {10.57702/ud58iifq},
    institution = {No Organization},
    keyword = {'Bayesian Networks', 'Correspondences', 'DMD', 'Data Augmentation', 'GANs', 'Generative Models', 'Image Classification', 'Image segmentation', 'Linear Models', 'Location-Aware', 'Machine Learning', 'Object Masking', 'Ornstein-Uhlenbeck process', 'Point Cloud', 'Potts model', 'Registration', 'Synthetic Data', 'Synthetic Data Generation', 'Synthetic data', 'Time Series', 'VAEs', 'contextual bandits', 'off-policy', 'question answering', 'single entity', 'single relation', 'synthetic data'},
    month = {dec},
    publisher = {TIB},
    title = {Synthetic Data},
    url = {https://service.tib.eu/ldmservice/dataset/synthetic-data},
    year = {2024}
}