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For example, while waiting for an interview, in line at a M-Den, or in an elevator on campus, don\u2019t be startled if a stranger says something to you like, \u201cDid you watch the FootballGame last night? What a game!\u201d They might also make a joke about the long line you\u2019re both in, or comment on the current situation. If you areinterested in learning more about \"small talk\", now it\u2019s your chance to sign up for this workshop on how to navigate small talk as an international student. In this workshop, we will educate you on what is small talk and you will also have the chance to practice small talk with Peers!", "occurrence_notes":null, "guid":"129760-21864530@events.umich.edu", "permalink":"http:\/\/events.umich.edu\/event\/129760", "building_id":"", "building_name":"", "campus_maps_id":"", "room":"", "location_name":"", "has_livestream":0, "cost":"", "tags":[], "website":"", "sponsors":[ { "group_name":"University Career Center UCC", "group_id":"1140", "website":"http:\/\/careercenter.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":1, "first_occurrence":21864530 } , "130777-21866873": { "datetime_modified":"20250108T212935", "datetime_start":"20250115T150000", "datetime_end":"20250115T160000", "has_end_time":1, "date_start":"2025-01-15", "date_end":"2025-01-15", "time_start":"15:00:00", "time_end":"16:00:00", "time_zone":"America\/Detroit", "event_title":"Probability and Analysis Seminar: Convergence of Unadjusted Langevin in High Dimensions: Delocalization of Bias", "occurrence_title":"", "combined_title":"Probability and Analysis Seminar: Convergence of Unadjusted Langevin in High Dimensions: Delocalization of Bias: Yifan Chen (New York University)", "event_subtitle":"Yifan Chen (New York University)", "event_type":"Workshop \/ Seminar", "event_type_id":"21", "description":"The unadjusted Langevin algorithm is commonly used to sample probability distributions in extremely high-dimensional settings. However, existing analyses of the algorithm for strongly log-concave distributions suggest that, as the dimension $d$ of the problem increases, the number of iterations required to ensure convergence within a desired error in the $W_2$ metric scales in proportion to $d$ or $\\sqrt{d}$. In this work, we argue that, despite this poor scaling of the $W_2$ error for the full set of variables, the behavior for a \\emph{small number} of variables can be significantly better: a number of iterations proportional to $K$, up to logarithmic terms in $d$, often suffices for the algorithm to converge to within a desired $W_2$ error for all $K$-marginals. We refer to this effect as \\textit{delocalization of bias}. We show that the delocalization effect does not hold universally and prove its validity for Gaussian distributions and strongly log-concave distributions with certain sparse interactions. Our analysis relies on a novel $W_{2,\\ell^\\infty}$ metric to measure convergence. A key technical challenge we address is the lack of a one-step contraction property in this metric. 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