[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128632-en":3,"doc-seo-128632-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128632,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Column Generation in Machine Learning","The thesis investigates the integration of operational research and machine learning to improve both accuracy and practical usability within ML frameworks through a strategic use of column-generation algorithms. Despite the widespread deployment of ML algorithms in pattern recognition applications, limited interpretability and heuristic dependence reduce reliability, motivating more refined methodologies. The study pursues two goals: evaluating column-generation-based classifiers in ML, and addressing pragmatic obstacles to their seamless adoption in real-world scenarios via algorithm development, heuristic improvement, and supporting software frameworks.","Titre:   \n Column Generation in Machine Learning  Title:   \nAuteur:   \n Krunal Kishor Patel  \n Author:   Date:  2024   \n Type:  Mémoire ou thèse / Dissertation or Thesis  Référence:  Patel, K. K. (2024) . Column Generation in Machine Learning [Thèse de doctorat, Citation:  Polytechnique Montréal] . PolyPublie. [https://publications.polymtl.ca/58317/](https://publications.polymtl.ca/58317/)  \nDocument en libre accès dans PolyPublie  \nOpen Access document in PolyPublie  \n| URL de PolyPublie:\u003Cbr>PolyPublie URL: | [https://publications.polymtl.ca/58317/](https://publications.polymtl.ca/58317/) |\n| --- | --- |\n| Directeurs de recherche:\u003Cbr>Advisors: | Guy Desaulniers, & Andrea Lodi |\n| Programme:\u003Cbr>  Program: | Doctorat en mathématiques |\n\nCe fichier a été téléchargé à partir de PolyPublie, le dépôt institutionnel de Polytechnique Montréal  \nThis file has been downloaded from PolyPublie, the institutional repository of Polytechnique Montréal  \n[https://publications.polymtl.ca](https://publications.polymtl.ca)  \nPOLYTECHNIQUE MONTRÉAL  \naffiliée à l’Université de Montréal  \nColumn Generation in Machine Learning  \nKRUNAL KISHOR PATEL  \nDépartement de mathématiques et de génie industriel  \nThèse présentée en vue de l’obtention du diplôme de Philosophiæ Doctor  \nMathématiques  \nAvril 2024  \n© Krunal Kishor Patel, 2024 .  \nPOLYTECHNIQUE MONTRÉAL  \naffiliée à l’Université de Montréal  \nCette thèse intitulée :  \nColumn Generation in Machine Learning  \nprésentée par Krunal Kishor PATEL en vue de l’obtention du diplôme de Philosophiæ Doctor a été dûment acceptée par le jury d’examen constitué de :  \nLouis-Martin ROUSSEAU, président Guy DESAULNIERS, membre et directeur de recherche Andrea LODI, membre et codirecteur de recherche Quentin CAPPART, membre  \nStefano GUALANDI, membre externe  \niii  \nDEDICATION  \nTo those who cheered me on with every step I took, And to the doubters, your skepticism was the perfect hook.  \niv  \nACKNOWLEDGEMENTS  \nI would like to express my deepest gratitude to my supervisors, Prof. Andrea Lodi and Prof. Guy Desaulniers, for their invaluable guidance, support, and encouragement throughout my PhD journey. Their expertise and mentorship have been instrumental in shaping this research. Special thanks to Andrea for making extra efforts to get me started with the Ph.D. program at Polytechnique Montreal.  \nI am grateful to Mehdi Taobane and Dr. Khalid Laaziri from the Canada Excellence Research Chair (CERC) for their assistance and support during my time at CERC. Mehdi’s facilitation of communication and Khalid’s provision of computational resources were essential for the success of this research.  \nSpecial thanks to my parents for their unwavering love and encouragement, as well as to my friends from my undergrad studies and school for their emotional support throughout this endeavor. Also thanks to my fellow PhD students, postdocs, and employees working nearby me for their camaraderie and support during my time at CERC.  \nI would also like to acknowledge the members of the chess club \"PolyÉchecs\" for the enjoyable chess games and moments of relaxation during my time at Polytechnique.  \nI would also like to thank the committee for spending their valuable time to read my thesis and provide useful feedback.  \nFinally, I am grateful to all those who have contributed to my academic and personal growth, even if their names are not mentioned here.  \nFunding for this research was provided by the Canada Excellence Research Chair (CERC) .  \nv  \nRÉSUMÉ  \nCette thèse explore l’intrégation de la recherche opérationnelle et de l’apprentissage automatique, mettant l’accent sur l’augmentation de la précision et de la praticalité au sein des cadres d’apprentissage automatique (ML) grâce à l’intégration stratégique d’algorithmes degénération de colonnes. Malgré l’ubiquité des algorithmes ML dans les applications de reconnaissance de motifs, leur manque d’interprétabilité et leur dépendance aux heuristiques introduisent de l’imprécision, sou","cbCaii5AqT8c5uoY","https://ap.wps.com/l/cbCaii5AqT8c5uoY","pdf",2075837,3,1,144,"English","en",105,"# Résumé\n## Objectives and research questions\n## Literature review (Chapter 2)\n## Contributions for decision rules (Chapter 3)\n## Heuristic improvements for classification trees (Chapter 4)","[{\"question\":\"What problem does the thesis address regarding machine learning methods?\",\"answer\":\"It addresses limitations in current ML approaches, especially reduced precision due to lack of interpretability and dependence on heuristics, which motivates more refined methodologies.\"},{\"question\":\"What are the thesis’s two main research objectives?\",\"answer\":\"First, to examine the use of column-generation-based classifiers in ML; second, to tackle practical challenges that prevent smooth integration of these classifiers into real scenarios.\"},{\"question\":\"What does the thesis contribute besides applying column generation?\",\"answer\":\"It includes an extensive literature review and proposes specific contributions such as accelerating column generation using CP-SAT, reducing the search space in the subproblem, and improving heuristics for learning classification trees.\"}]","Column Generation in Machine Learning | PDF",1786002206,363,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"column-generation-in-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/column-generation-in-machine-learning/128632/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the thesis address regarding machine learning methods?","Question",{"text":76,"@type":77},"It addresses limitations in current ML approaches, especially reduced precision due to lack of interpretability and dependence on heuristics, which motivates more refined methodologies.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What are the thesis’s two main research objectives?",{"text":81,"@type":77},"First, to examine the use of column-generation-based classifiers in ML; 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