{"id":56067,"date":"2026-04-06T22:33:26","date_gmt":"2026-04-07T02:33:26","guid":{"rendered":"https:\/\/engineering.jhu.edu\/ams\/?post_type=tribe_events&#038;p=56067"},"modified":"2026-04-06T22:33:26","modified_gmt":"2026-04-07T02:33:26","slug":"ams-weekly-seminar-jesus-fernandez-villaverde","status":"publish","type":"tribe_events","link":"https:\/\/engineering.jhu.edu\/ams\/event\/ams-weekly-seminar-jesus-fernandez-villaverde\/","title":{"rendered":"AMS Weekly Seminar | Jesus Fernandez-Villaverde"},"content":{"rendered":"<p><strong>Location: <\/strong>Shaffer 3<\/p>\n<p><strong>When:<\/strong> April 9th at 1:30 p.m.<\/p>\n<p><strong>Title: <\/strong>The Blessings of Overparameterization<\/p>\n<p><span><strong>Abstract: <\/strong>We study the behavior of overparameterized neural networks in solving economic dynamic programming problems. Using two canonical examples (a McCall job search model and a Linear-Quadratic Regulator applied to a firm investment problem with adjustment costs), we show that overparameterization yields three key benefits: Bellman and Euler residuals decline monotonically with the number of parameters; approximate solutions converge to closed-form solutions with relative errors below 0.001; and optimization becomes increasingly stable across random initializations. These results contrast with standard regression problems, where test loss plateaus while train loss continues to fall. The self-referential structure of Bellman equations appears to interact favorably with overparameterization in ways absent from standard supervised learning. Results are robust across multiple function approximation families, including neural networks with alternative activation functions, Random Fourier Features, and Chebyshev polynomials.<\/span><\/p>\n<p><strong>Zoom link:<\/strong> https:\/\/wse.zoom.us\/j\/92366532431<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Location: Shaffer 3 When: April 9th at 1:30 p.m. Title: The Blessings of Overparameterization Abstract: We study the behavior of overparameterized neural networks in solving economic dynamic programming problems. Using&hellip;<\/p>\n","protected":false},"author":69,"featured_media":0,"template":"","meta":{"_acf_changed":false,"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"tribe_events_cat":[260],"class_list":["post-56067","tribe_events","type-tribe_events","status-publish","hentry","tribe_events_cat-seminars-and-endowed-lectures","cat_seminars-and-endowed-lectures"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>AMS Weekly Seminar | Jesus Fernandez-Villaverde | Department of Applied Mathematics and Statistics<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/engineering.jhu.edu\/ams\/event\/ams-weekly-seminar-jesus-fernandez-villaverde\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AMS Weekly Seminar | Jesus Fernandez-Villaverde | Department of Applied Mathematics and Statistics\" \/>\n<meta property=\"og:description\" content=\"Location: Shaffer 3 When: April 9th at 1:30 p.m. Title: The Blessings of Overparameterization Abstract: We study the behavior of overparameterized neural networks in solving economic dynamic programming problems. Using&hellip;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/engineering.jhu.edu\/ams\/event\/ams-weekly-seminar-jesus-fernandez-villaverde\/\" \/>\n<meta property=\"og:site_name\" content=\"Department of Applied Mathematics and Statistics\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"AMS Weekly Seminar | Jesus Fernandez-Villaverde | Department of Applied Mathematics and Statistics","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/engineering.jhu.edu\/ams\/event\/ams-weekly-seminar-jesus-fernandez-villaverde\/","og_locale":"en_US","og_type":"article","og_title":"AMS Weekly Seminar | Jesus Fernandez-Villaverde | Department of Applied Mathematics and Statistics","og_description":"Location: Shaffer 3 When: April 9th at 1:30 p.m. Title: The Blessings of Overparameterization Abstract: We study the behavior of overparameterized neural networks in solving economic dynamic programming problems. 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