{"id":50977,"date":"2025-01-17T11:45:56","date_gmt":"2025-01-17T16:45:56","guid":{"rendered":"https:\/\/engineering.jhu.edu\/ams\/?post_type=tribe_events&p=50977"},"modified":"2025-02-24T11:43:24","modified_gmt":"2025-02-24T16:43:24","slug":"ams-weekly-seminar-hamsa-bastani","status":"publish","type":"tribe_events","link":"https:\/\/engineering.jhu.edu\/ams\/event\/ams-weekly-seminar-hamsa-bastani\/","title":{"rendered":"AMS Weekly Seminar | Hamsa Bastani"},"content":{"rendered":"

Location:\u00a0<\/strong>Gilman 50<\/p>\n

When:<\/strong>\u00a0February 27th at 1:30 p.m.<\/p>\n

Title:\u00a0<\/strong>Multitask Learning and Bandits via Robust Statistics<\/span><\/p>\n

Abstract:\u00a0<\/strong>We study the “many bandits” setting, where a decision-maker must simultaneously learn across multiple distinct but related linear contextual bandit instances. Such problems arise in a number of<\/span>\u00a0applications, including personalized medicine, targeted COVID-19 screening, dynamic pricing, and adaptive auditing. We study the setting where the unknown parameter in each bandit instance can be decomposed into a global parameter plus a sparse instance-specific term. Then, we propose a novel two-stage estimator that\u00a0exploits this structure in a sample-efficient way by\u00a0using a combination of robust statistics (to learn across similar instances) and LASSO regression (to debias the results). We embed this estimator within a bandit algorithm, and<\/span> prove that it improves asymptotic regret bounds in the context dimension; this improvement is exponential for data-poor instances. We further demonstrate how our results depend on the underlying network structure of bandit instances. Joint work with Kan Xu.<\/span><\/p>\n

Zoom link:<\/strong> https:\/\/wse.zoom.us\/j\/93287142219?pwd=z9fqWnRMzmzS0SGijRiie5yN3kHRSZ.1<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Location:\u00a0Gilman 50 When:\u00a0February 27th at 1:30 p.m. Title:\u00a0Multitask Learning and Bandits via Robust Statistics Abstract:\u00a0We study the “many bandits” setting, where a decision-maker must simultaneously learn across multiple distinct 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