{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/non-count-symmetries-in-boolean-multi-valued","title":"Non-Count Symmetries in Boolean & Multi-Valued Prob. Graphical Models","arxiv_id":"1707.08879","date":"2017-07-27","proceeding":null,"authors":["Ankit Anand","Ritesh Noothigattu","Parag Singla","Mausam"],"abstract":"Lifted inference algorithms commonly exploit symmetries in a probabilistic\ngraphical model (PGM) for efficient inference. However, existing algorithms for\nBoolean-valued domains can identify only those pairs of states as symmetric, in\nwhich the number of ones and zeros match exactly (count symmetries). Moreover,\nalgorithms for lifted inference in multi-valued domains also compute a\nmulti-valued extension of count symmetries only. These algorithms miss many\nsymmetries in a domain. In this paper, we present first algorithms to compute\nnon-count symmetries in both Boolean-valued and multi-valued domains. Our\nmethods can also find symmetries between multi-valued variables that have\ndifferent domain cardinalities. The key insight in the algorithms is that they\nchange the unit of symmetry computation from a variable to a variable-value\n(VV) pair. Our experiments find that exploiting these symmetries in MCMC can\nobtain substantial computational gains over existing algorithms.","url_abs":"http://arxiv.org/abs/1707.08879v1","url_pdf":"http://arxiv.org/pdf/1707.08879v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"non-count-symmetries-in-boolean-multi-valued","repo_url":"https://github.com/dair-iitd/nc-mcmc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}