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No matter who you are, where you come from, or what you\u2019re studying, a global experience is available to you during your time at Michigan.\n\nGet your questions answered! 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"tags":["Sessions"], "website":"https:\/\/sessions.studentlife.umich.edu\/p\/track\/17025", "sponsors":[ { "group_name":"Sessions @ Michigan", "group_id":"4791", "website":"https:\/\/sessions.studentlife.umich.edu\/" } ], "image_url":"", "styled_images":{ "event_thumb":"", "event_large":"", "event_large_2x":"", "event_large_lightbox":"", "group_thumb":"", "group_thumb_square":"", "group_large":"", "group_large_lightbox":"", "event_large_crop":"", "event_list":"", "event_list_2x":"", "event_grid":"", "event_grid_2x":"", "event_feature_large":"", "event_feature_thumb":"" }, "occurrence_count":4, "first_occurrence":21901225 } , "141373-21888712": { "datetime_modified":"20260407T124520", "datetime_start":"20261007T160000", "datetime_end":"20261007T170000", "has_end_time":1, "date_start":"2026-10-07", "date_end":"2026-10-07", "time_start":"16:00:00", "time_end":"17:00:00", "time_zone":"America\/Detroit", "event_title":"Robust and Risk-Sensitive Acceleration in Gradient Methods", 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In unconstrained problems with exact gradients, momentum-based methods\u2014most notably Nesterov\u2019s accelerated gradient descent (AGD) and Polyak\u2019s heavy-ball (HB) method\u2014achieve faster convergence by improving dependence on the condition number. However, this acceleration comes at a cost: momentum amplifies gradient noise, making these methods less robust than GD under standard parameter choices and requiring more accurate gradient estimates to attain comparable accuracy. Similar challenges arise in convex and nonconvex min\u2013max optimization.\nMotivated by applications in machine learning, this talk studies unconstrained and min\u2013max optimization under deterministic, unbiased stochastic, and biased stochastic gradient noise. 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