Projects per year
Abstract
Probabilistic representations, such as Bayesian and Markov networks, are fundamental to much of statistical machine learning. Thus, learning probabilistic representations directly from data is a deep challenge, the main computational bottleneck being inference that is intractable. Tractable learning is a powerful new paradigm that attempts to learn distributions that support efficient probabilistic querying. By leveraging local structure, representations such as sumproduct networks (SPNs) can capture high treewidth models with many hidden layers, essentially a deep architecture, while still admitting a range of probabilistic queries tobe computable in time polynomial in the network size. While the progress is impressive, numerous data sources are incomplete, and in the presence of missing data, structure learning methods nonetheless revert to single distributions without characterizing the loss in confidence. In recent work, credal sumproduct networks, an imprecise extension of sumproduct networks, were proposed to capture this robustness angle. In this work, we are interested in how such representations can be learnt and thus study how the computational machinery underlying tractable learning and inference can be generalized for imprecise probabilities.
Original language  English 

Number of pages  24 
Publication status  Published  22 Jun 2020 
Event  2nd Conference on Automated Knowledge Base Construction 2020  Online conference Duration: 22 Jun 2020 → 24 Jun 2020 Conference number: 2 https://www.akbc.ws/2020/ 
Conference
Conference  2nd Conference on Automated Knowledge Base Construction 2020 

Abbreviated title  AKBC 2020 
Period  22/06/20 → 24/06/20 
Internet address 
Fingerprint
Dive into the research topics of 'Learning Credal SumProduct Networks'. Together they form a unique fingerprint.Projects
 1 Finished

Towards Explainable and Robust Statistical AI: A Symbolic Approach
15/06/18 → 14/09/19
Project: Research