mlsanityData to 14 Oct 2026

Consistent Joint Decision-Making with Heterogeneous Learning Models

arXiv 2402.03728 ↗First posted 6 Feb 2024cs.AI cs.CL cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 24 of its 38 references that are in the map. First posted on arXiv 6 Feb 2024.

Builds on

24 of 38 references.

References · 24

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12 more
  1. 2016Guided Open Vocabulary Image Captioning with Constrained Beam Searchcited by 105
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024Prompt2DeModel: Declarative Neuro-Symbolic Modeling with Natural Languagecited by 3
  2. 2026Verb-ICL: Rethinking In-Context Learning for Structured Predictioncited by 0