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Inference of unknown opinions with uncertain, adversarial (e.g., incorrect or conflicting) evidence in large datasets is not a trivial task. Without proper handling, it can easily mislead decision making in data mining tasks. In this work, we propose a highly scalable opinion inference probabilistic model, namely Adversarial Collective Opinion Inference (Adv-COI), which provides a solution to infer unknown opinions with high scalability and robustness under the presence of uncertain, adversarial evidence by enhancing Collective Subjective Logic (CSL) which is developed by combining SL and Probabilistic Soft Logic (PSL). The key idea behind the Adv-COI is to learn a model of robust ways against uncertain, adversarial evidence which is formulated as a min-max problem. We validate the out-performance of the Adv-COI compared to baseline models and its competitive counterparts under possible adversarial attacks on the logic-rule based structured data and white and black box adversarial attacks under both clean and perturbed semi-synthetic and real-world datasets in three real world applications. The results show that the Adv-COI generates the lowest mean absolute error in the expected truth probability while producing the lowest running time among all.
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Negative Weights in Hinge-Loss Markov Random Fields.
Probabilistic soft logic (PSL) is a framework for instantiating probabilistic graphical models (PGM) representing complex relational data. Weighted first-order logical statements are used as templates for creating potential functions which define the PGM density. Traditionally, PSL constrains weights to be non-negative to ensure maximum a posteriori (MAP) inference is a tractable convex optimization problem. We propose three novel approaches to extending PSL's expressivity to allow negative weights. Notably, we propose the use of Gödel logic for defining potentials from negatively weighted rules. This method improves upon prior work on this topic by preserving both the convexity and scale of the MAP inference problem. Moreover, we show where each of the five methods discussed in this paper overlap and where they most differ. All negative methods are implemented in PSL, and we introduce a tunable synthetic dataset designed to empirically compare the performance of predictions.
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- Award ID(s):
- 2023495
- PAR ID:
- 10282805
- Date Published:
- Journal Name:
- Workshop on Tractable Probabilistic Modeling (TPM)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)Using unreliable information sources generating conflicting evidence may lead to a large uncertainty, which significantly hurts the decision making process. Recently, many approaches have been taken to integrate conflicting data from multiple sources and/or fusing conflicting opinions from different entities. To explicitly deal with uncertainty, a belief model called Subjective Logic (SL), as a variant of Dumpster-Shafer Theory, has been proposed to represent subjective opinions and to merge multiple opinions by offering a rich volume of fusing operators, which have been used to solve many opinion inference problems in trust networks. However, the operators of SL are known to be lack of scalability in inferring unknown opinions from large network data as a result of the sequential procedures of merging multiple opinions. In addition, SL does not consider deriving opinions in the presence of conflicting evidence. In this work, we propose a hybrid inference method that combines SL and Probabilistic Soft Logic (PSL), namely, Collective Subjective Plus, CSL + , which is resistible to highly conflicting evidence or a lack of evidence. PSL can reason a belief in a collective manner to deal with large-scale network data, allowing high scalability based on relationships between opinions. However, PSL does not consider an uncertainty dimension in a subjective opinion. To take benefits from both SL and PSL, we proposed a hybrid approach called CSL + for achieving high scalability and high prediction accuracy for unknown opinions with uncertainty derived from a lack of evidence and/or conflicting evidence. Through the extensive experiments on four semi-synthetic and two real-world datasets, we showed that the CSL + outperforms the state-of-the-art belief model (i.e., SL), probabilistic inference models (i.e., PSL, CSL), and deep learning model (i.e., GCN-VAE-opinion) in terms of prediction accuracy, computational complexity, and real running time.more » « less
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