Projects per year
Abstract / Description of output
Incorporating constraints is a major concern in probabilistic machine learning. A wide variety of problems require predictions to be integrated with reasoning about constraints, from modeling routes on maps to approving loan predictions. In the former, we may require the prediction model to respect the presence of physical paths between the nodes on the map, and in the latter, we may require that the prediction model respect fairness constraints that ensure that outcomes are not subject to bias. Broadly speaking, constraints may be probabilistic, logical or causal, but the overarching challenge is to determine if and how a model can be learnt that handles a declared constraint. To the best of our knowledge, treating this in a general way is largely an open problem. In this paper, we investigate how the learning of sum-product networks, a newly introduced and increasingly popular class of tractable probabilistic models, is possible with declared constraints. We obtain correctness results about the training of these models, by establishing a relationship between probabilistic constraints and the model's parameters.
Original language | English |
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Article number | 644062 |
Number of pages | 11 |
Journal | Frontiers in Artificial Intelligence |
Volume | 4 |
DOIs | |
Publication status | Published - 8 Apr 2021 |
Keywords / Materials (for Non-textual outputs)
- sum-product network
- constraints
- tractable models
- optimization
- machine learning
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Dive into the research topics of 'Closed-Form Results for Prior Constraints in Sum-Product Networks'. Together they form a unique fingerprint.Projects
- 1 Finished
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Towards Explainable and Robust Statistical AI: A Symbolic Approach
15/06/18 → 14/09/19
Project: Research