[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-657491-105":59,"doc-detail-657491-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","obdd-net-end-to-end-learning-of-ordered-binary-decision-diagrams","OBDD-NET - End-to-End Learning of Ordered Binary Decision Diagrams","","Learning Ordered Binary Decision Diagrams (OBDDs) from large-scale datasets is a key task in explainable artificial intelligence, yet existing search-based methods do not scale because they must explicitly encode dataset-wide example satisfaction. The work proposes an OBDD encoding that parameterizes a neural network, separating satisfaction encoding from all-example enumeration and improving training efficiency via mini-batches. The paper proves continuous-space simulation of OBDD inference, introduces faithful encoding constraints for interpretability, and reports strong scalability and competitive predictive performance.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/obdd-net-end-to-end-learning-of-ordered-binary-decision-diagrams/657491/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/obdd-net-end-to-end-learning-of-ordered-binary-decision-diagrams/657491.png","ImageObject",300,407,{"name":92,"@type":93},"notyourbestiee","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-10-26",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does OBDD-NET address in OBDD learning?","Question",{"text":112,"@type":113},"It addresses the scalability limitation of existing search-based OBDD learning, which must explicitly encode the satisfaction of all examples in a dataset.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does OBDD-NET improve learning efficiency?",{"text":117,"@type":113},"It introduces a neural-network parameterization scheme that frees dataset-wide satisfaction encoding and leverages mini-batch training to enhance efficiency.",{"name":119,"@type":110,"acceptedAnswer":120},"What is the main theoretical contribution of the approach?",{"text":121,"@type":113},"The paper proves that the proposed method can simulate OBDD inference within a continuous space, enabling end-to-end training.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},657491,1792981004,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":14,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":143},962094243716,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","OBDD-NET: End-to-End Learning of Ordered Binary Decision  \nDiagrams  \nJunming Qiu  \nSun Yat-sen University Guangzhou, China Peng Cheng Laboratory Shenzhen, China [qiujm9@mail2.sysu.edu.cn](qiujm9@mail2.sysu.edu.cn)  \nRongzhen Ye  \nSun Yat-sen University Guangzhou, China [yerzh@mail2.sysu.edu.cn](yerzh@mail2.sysu.edu.cn)  \nWeilin Luo  \nSun Yat-sen University Guangzhou, China [luowlin5@mail.sysu.edu.cn](luowlin5@mail.sysu.edu.cn)  \nKunxun Qi  \nSun Yat-sen University Guangzhou, China [kunxunqi@foxmail.com](kunxunqi@foxmail.com)  \nHai Wan∗ Sun Yat-sen University Guangzhou, China [wanhai@mail.sysu.edu.cn](wanhai@mail.sysu.edu.cn)  \nYue Yu∗ Peng Cheng Laboratory Shenzhen, China [yuy@pcl.ac.cn](yuy@pcl.ac.cn)  \nAbstract  \nLearning Ordered Binary Decision Diagrams (OBDDs) from largescale datasets is an important topic of explainable artificial intelligence. However, existing search-based methods are still limited inscalability regarding dataset size, since they must explicitly encode the satisfaction of all examples in a dataset. To tackle this challenge, we introduce an OBDD encoding method to parameterize a neural network. This method frees satisfaction encoding of all examples in a dataset while leveraging mini-batch training techniques to enhance learning efficiency. Our main theoretical contribution is to prove that our approach enables the simulation of OBDD inference within a continuous space. Besides, we identify faithful OBDD encoding to fulfill the properties required by OBDDs, allowing to interpret an OBDD directly from the learned parameter assignment. With faithful OBDD encoding, we present an end-to-end neural model named OBDD-NET, being capable of coping with largescale datasets. Experimental results exhibit better scalability and competitive prediction performance of OBDD-NET compared to stateof-the-art OBDD learners. Valuable insights about faithful OBDD encoding are derived from the ablation study. The implementation is available at: [https://github.com/jmq-design/OBDD-NET](https://github.com/jmq-design/OBDD-NET).  \nCCS Concepts  \n• Computing methodologies → Rule learning.  \nKeywords  \nOBDD learning, End-to-End learning, Faithful encoding  \n∗ Both authors are corresponding authors.  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission [and/or a fee. Request permissions from permissions@acm.org](and/or a fee. Request permissions from permissions@acm.org).  \nCIKM’25, Seoul, Republic of Korea  \n© 2025 Copyright held by the owner/author(s) . Publication rights licensed to ACM. ACM ISBN 979-8-4007-2040-6/2025/11  \n[https://doi.org/10.1145/3746252.3761195](https://doi.org/10.1145/3746252.3761195)  \nACM Reference Format:  \nJunming Qiu, Rongzhen Ye, Weilin Luo, Kunxun Qi, Hai Wan, and Yue Yu. 2025. OBDD-NET: End-to-End Learning of Ordered Binary Decision Diagrams. In Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM’25), November 10–14, 2025, Seoul, Republic of Korea. ACM, New York, NY, USA, 10 pages. [https://doi](https://doi). org/10.1145/3746252.3761195  \n1 Introduction  \nLearning an interpretable model from large-scale data is a crucial topic in eXplainable Artificial Intelligence (XAI) . Compared to (Binary) Decision Tree (DT), Binary Decision Diagram (BDD) is amore compact interpretable model due to the node sharing [5] . In particular, Ordered BDD (OBDD) [4], a tractable subset of BDD, has recently received increasing attention in XAI [5, 15, 23, 24, 33] . The main attractions of the OBDD representation o","cbCaibuHk6fbP97i","https://ap.wps.com/l/cbCaibuHk6fbP97i","pdf",1459553,"English","# Introduction\n## OBDD representation in XAI\n## Ordering property and OBDD depth/size\n## Learning challenges with limited depth\n## Prior work on limited-depth OBDD learning","[{\"question\":\"What problem does OBDD-NET address in OBDD learning?\",\"answer\":\"It addresses the scalability limitation of existing search-based OBDD learning, which must explicitly encode the satisfaction of all examples in a dataset.\"},{\"question\":\"How does OBDD-NET improve learning efficiency?\",\"answer\":\"It introduces a neural-network parameterization scheme that frees dataset-wide satisfaction encoding and leverages mini-batch training to enhance efficiency.\"},{\"question\":\"What is the main theoretical contribution of the approach?\",\"answer\":\"The paper proves that the proposed method can simulate OBDD inference within a continuous space, enabling end-to-end training.\"}]","OBDD-NET - End-to-End Learning of Ordered Binary Decision Diagrams | PDF",25]