[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119168-en":3,"doc-seo-119168-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119168,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Preparing for Black Swans - The Antifragility Imperative for Machine Learning","Operating safely and reliably despite continual distribution shifts is vital for high-stakes machine learning applications. The paper builds on “antifragility” as a constructive design paradigm that aims not only to withstand but to benefit from volatility. It formally defines antifragility for online decision making via dynamic regret and exposes limitations of methods centered on resisting nonstationarity. It then proposes computational pathways grounded in online learning, linking to meta-learning, safe exploration, continual learning, multi-objective optimization, and foundation models, while outlining guidelines and risk assessment for responsible deployment.","arXiv :2405 . 11397v1 [ cs .LG] 18 May 2024  \nPreparing for Black Swans : The Antifragility Imperative for Machine Learning  \nMing Jin∗  \nAbstract  \nOperating safely and reliably despite continual distribution shifts is vital for highstakes machine learning applications. This paper builds upon the transformative concept of “antifragility” introduced by (Taleb, 2014) as a constructive design paradigm to not just withstand but benefit from volatility. We formally define antifragility in the context of online decision making as dynamic regret’s strictly concave response to environmental variability, revealing limitations of current approaches focused on resisting rather than benefiting from nonstationarity. Our contribution lies in proposing potential computational pathways for engineering antifragility, grounding the concept in online learning theory and drawing connections to recent advancements in areas such as meta-learning, safe exploration, continual learning, multi-objective/quality-diversity optimization, and foundation models. By identifying promising mechanisms and future research directions, we aim to put antifragility on a rigorous theoretical foundation in machine learning. We further emphasize the need for clear guidelines, risk assessment frameworks, and interdisciplinary collaboration to ensure responsible application.  \n1 Introduction  \nAs we integrate machine learning (ML) into mission-critical systems in healthcare, finance, transportation, and social-scale infrastructure like power grids, a vital question arises about ensuring safety, security, and reliability despite myriad stressors (Hendrycks et al., 2021b) . These manifest as natural or adversarial perturbations, aleatoric/epistemic uncertainties, distribution shifts (including domain shift, concept drift, nonstionarity, and out-of-distribution events) .  \nThe prevailing paradigm in ML pursues robustness—shielding systems against these stressors. However, as S. Sagan cautions, “Things that have never happened before happen all the time.” Novel outliers routinely breach reactive defenses, raising concerns about their limitations against rare but impactful “black swan” events (Taleb, 2010) . (Nair et al., 2022) present statistical arguments about why such long-tailed phenomena prove unexpectedly ubiquitous.  \nRather than merely withstand stressors, can systems be designed to thrive because of them?  \nThis inverted perspective of embracing rather than barricading against disorder constitutes the radical notion of “antifragility.” As originally defined by (Taleb, 2014):  \n∗ This work was supported in part by the NSF Safe Learning-Enabled Systems Program (NSF \\# 2331775) .  \n“Some things benefit from shocks; they thrive and grow when exposed to volatility, randomness, disorder, and stressors and love adventure, risk, and uncertainty.  \nYet, in spite of the ubiquity of the phenomenon, there is no word for the exact opposite of fragile. Let us call it antifragile. Antifragility is beyond resilience or robustness. The resilient resists shocks and stays the same; the antifragile gets better. ”  \nDespite inspiring examples across various domains, formal frameworks in ML leveraging antifragility remain scarce. This manuscript proposes a rigorous definition of antifragility based on the online decision-making (ODM) framework and identifies potential computational pathways to realize this vision.  \n1.1 Perspective  \nWe see antifragility as both an attainable goal and a constructive design solution for ML systems.  \nAntifragile systems embody an open-world perspective, actively leveraging continual stressors such as distributional shifts to systematically transform them into opportunities for enhancement. These systems are capable not just of adaptation, but of rapid adaptation with improvisation when faced with unforeseen disruptions. They exhibit unusual robustness that goes beyond typical stressors to confront potentially unforeseeable, high-impact challenges. This i","cbCaihXqBwgd8hHa","https://ap.wps.com/l/cbCaihXqBwgd8hHa","pdf",587867,1,25,"English","en",105,"# Introduction\n## Perspective\n## Rejoinders","[{\"question\":\"What does “antifragility” mean in this paper’s machine learning context?\",\"answer\":\"Antifragility is framed as a goal and design paradigm where systems do not merely resist shocks from distribution shifts and uncertainties; they transform them into opportunities for improvement through exposure and learning.\"},{\"question\":\"How is antifragility formally defined for online decision making here?\",\"answer\":\"The paper defines antifragility using online decision-making theory, characterizing it through dynamic regret with a strictly concave response to environmental variability.\"},{\"question\":\"How does the paper address safety and risk when using volatility in machine learning?\",\"answer\":\"It argues for measured openness via safe environment interaction and selective harnessing under safety constraints, enabling recoverable failures and using simulated or small-scale disturbances in critical domains, supported by guidelines and risk assessment frameworks.\"}]","Preparing for Black Swans - The Antifragility Imperative for Machine Learning | PDF",1785722884,63,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"preparing-for-black-swans-the-antifragility-imperative-for-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/preparing-for-black-swans-the-antifragility-imperative-for-machine-learning/119168/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does “antifragility” mean in this paper’s machine learning context?","Question",{"text":75,"@type":76},"Antifragility is framed as a goal and design paradigm where systems do not merely resist shocks from distribution shifts and uncertainties; they transform them into opportunities for improvement through exposure and learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is antifragility formally defined for online decision making here?",{"text":80,"@type":76},"The paper defines antifragility using online decision-making theory, characterizing it through dynamic regret with a strictly concave response to environmental variability.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the paper address safety and risk when using volatility in machine learning?",{"text":84,"@type":76},"It argues for measured openness via safe environment interaction and selective harnessing under safety constraints, enabling recoverable failures and using simulated or small-scale disturbances in critical domains, supported by guidelines and risk assessment frameworks.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]