Projects per year
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 sum-product networks (SPNs) can capture high tree-width 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 sum-product networks, an imprecise extension of sum-product 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.
|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
|Conference||2nd Conference on Automated Knowledge Base Construction 2020|
|Abbreviated title||AKBC 2020|
|Period||22/06/20 → 24/06/20|
FingerprintDive into the research topics of 'Learning Credal Sum-Product Networks'. Together they form a unique fingerprint.
- 1 Finished
15/06/18 → 14/09/19