xInv: Explainable Optimization of Inverse Problems
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915354499874816 |
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| author | Memery, Sean Denamganai, Kevin Kapron-King, Anna Subr, Kartic |
| author_facet | Memery, Sean Denamganai, Kevin Kapron-King, Anna Subr, Kartic |
| contents | Inverse problems are central to a wide range of fields, including healthcare, climate science, and agriculture. They involve the estimation of inputs, typically via iterative optimization, to some known forward model so that it produces a desired outcome. Despite considerable development in the explainability and interpretability of forward models, the iterative optimization of inverse problems remains largely cryptic to domain experts. We propose a methodology to produce explanations, from traces produced by an optimizer, that are interpretable by humans at the abstraction of the domain. The central idea in our approach is to instrument a differentiable simulator so that it emits natural language events during its forward and backward passes. In a post-process, we use a Language Model to create an explanation from the list of events. We demonstrate the effectiveness of our approach with an illustrative optimization problem and an example involving the training of a neural network. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11056 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | xInv: Explainable Optimization of Inverse Problems Memery, Sean Denamganai, Kevin Kapron-King, Anna Subr, Kartic Machine Learning Artificial Intelligence I.2.7 Inverse problems are central to a wide range of fields, including healthcare, climate science, and agriculture. They involve the estimation of inputs, typically via iterative optimization, to some known forward model so that it produces a desired outcome. Despite considerable development in the explainability and interpretability of forward models, the iterative optimization of inverse problems remains largely cryptic to domain experts. We propose a methodology to produce explanations, from traces produced by an optimizer, that are interpretable by humans at the abstraction of the domain. The central idea in our approach is to instrument a differentiable simulator so that it emits natural language events during its forward and backward passes. In a post-process, we use a Language Model to create an explanation from the list of events. We demonstrate the effectiveness of our approach with an illustrative optimization problem and an example involving the training of a neural network. |
| title | xInv: Explainable Optimization of Inverse Problems |
| topic | Machine Learning Artificial Intelligence I.2.7 |
| url | https://arxiv.org/abs/2506.11056 |