Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.29691 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911557802262528 |
|---|---|
| author | Marwitz, Florian Andreas Braun, Tanya Möller, Ralf |
| author_facet | Marwitz, Florian Andreas Braun, Tanya Möller, Ralf |
| contents | Real world scenarios can be captured with lifted probability distributions. However, distributions are usually encoded in a table or list, requiring an exponential number of values. Hence, we propose a method for extracting first-order formulas from probability distributions that require significantly less values by reducing the number of values in a distribution and then extracting, for each value, a logical formula to be further minimized. This reduction and minimization allows for increasing the sparsity in the encoding while also generalizing a given distribution. Our evaluation shows that sparsity can increase immensely by extracting a small set of short formulas while preserving core information. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_29691 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A First Step Towards Even More Sparse Encodings of Probability Distributions Marwitz, Florian Andreas Braun, Tanya Möller, Ralf Artificial Intelligence Real world scenarios can be captured with lifted probability distributions. However, distributions are usually encoded in a table or list, requiring an exponential number of values. Hence, we propose a method for extracting first-order formulas from probability distributions that require significantly less values by reducing the number of values in a distribution and then extracting, for each value, a logical formula to be further minimized. This reduction and minimization allows for increasing the sparsity in the encoding while also generalizing a given distribution. Our evaluation shows that sparsity can increase immensely by extracting a small set of short formulas while preserving core information. |
| title | A First Step Towards Even More Sparse Encodings of Probability Distributions |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.29691 |