Consistent Joint Decision-Making with Heterogeneous Learning Models
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916116971913216 |
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| author | Faghihi, Hossein Rajaby Kordjamshidi, Parisa |
| author_facet | Faghihi, Hossein Rajaby Kordjamshidi, Parisa |
| contents | This paper introduces a novel decision-making framework that promotes consistency among decisions made by diverse models while utilizing external knowledge. Leveraging the Integer Linear Programming (ILP) framework, we map predictions from various models into globally normalized and comparable values by incorporating information about decisions' prior probability, confidence (uncertainty), and the models' expected accuracy. Our empirical study demonstrates the superiority of our approach over conventional baselines on multiple datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_03728 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Consistent Joint Decision-Making with Heterogeneous Learning Models Faghihi, Hossein Rajaby Kordjamshidi, Parisa Artificial Intelligence Computation and Language Machine Learning Logic in Computer Science This paper introduces a novel decision-making framework that promotes consistency among decisions made by diverse models while utilizing external knowledge. Leveraging the Integer Linear Programming (ILP) framework, we map predictions from various models into globally normalized and comparable values by incorporating information about decisions' prior probability, confidence (uncertainty), and the models' expected accuracy. Our empirical study demonstrates the superiority of our approach over conventional baselines on multiple datasets. |
| title | Consistent Joint Decision-Making with Heterogeneous Learning Models |
| topic | Artificial Intelligence Computation and Language Machine Learning Logic in Computer Science |
| url | https://arxiv.org/abs/2402.03728 |