Testing and learning on distributional and set inputs
<p>As machine learning gains significant attention in many disciplines and research communities, the variety of data structures has increased, with examples including distributions and sets of observations. In this thesis, we consider sets and distributions as inputs for machine learning pr...
Autor principal: | Law, H |
---|---|
Outros Autores: | Sejdinovic, D |
Formato: | Thesis |
Idioma: | English |
Publicado em: |
2019
|
Assuntos: |
Registos relacionados
-
Utilizing Statistical Tests for Comparing Machine Learning Algorithms
Por: Hozan Khalid Hamarashid
Publicado em: (2021-07-01) -
The information of attribute uncertainties: what convolutional neural networks can learn about errors in input data
Por: Natália V N Rodrigues, et al.
Publicado em: (2023-01-01) -
Towards trustworthy machine learning with kernels
Por: Chau, SL
Publicado em: (2023) -
Towards data-efficient deep learning with meta-learning and symmetries
Por: Xu, J
Publicado em: (2023) -
Kernel-based hypothesis tests: large-scale approximations and Bayesian perspectives
Por: Zhang, Q
Publicado em: (2019)