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X-ORIGINAL-URL:https://www.cmm.uchile.cl/
X-WR-CALNAME:CMM
X-WR-CALDESC:Centro de Modelamiento Matemático
X-WR-TIMEZONE:America/Santiago
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TZID:America/Santiago
X-LIC-LOCATION:America/Santiago
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0400
TZNAME:-04
DTSTART:20260531T044027
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BEGIN:VEVENT
CLASS:PUBLIC
UID:MEC-e57dbebc740250d2c4a370cf6ccb35f0@cmm.uchile.cl
DTSTART;TZID=America/Santiago:20260527T150000
DTEND;TZID=America/Santiago:20260527T180000
DTSTAMP:20260526T171650Z
CREATED:20260526
LAST-MODIFIED:20260526
PRIORITY:5
SEQUENCE:3
TRANSP:OPAQUE
SUMMARY:Chilean Probability Seminar: “On Model-Based Clustering with Entropic Optimal Transport”
DESCRIPTION:Abstract:\nResumen: We develop a new methodology for model-based clustering. Optimizing the log-likelihood provides a principled statistical framework for clustering, with solutions found via the EM algorithm. However, because the log-likelihood is nonconvex, only convergence to stationary points can be guaranteed, and practitioners often use multiple starting points in the hope that one will converge to the global solution. We consider a new loss function based on entropic optimal transport that shares the same global optimum as the log-likelihood but has a much better-behaved landscape, thereby avoiding spurious local-optima configurations that are pervasive with the log-likelihood. Similar to the EM algorithm for the log-likelihood, this new loss can be optimized by the Sinkhorn-EM algorithm, which we show converges at a rate comparable to that of EM. By analyzing extensive numerical experiments and two real-world applications in image segmentation in C. elegans microscopy and clustering in spatial transcriptomics, we show that this new loss outperforms log-likelihood optimization, indicating that it represents a valuable clustering methodology for practitioners. We also comment on finite-sample properties of this procedure, leveraging novel convergence bounds for objects arising from entropic optimal transport.\nSpeaker:  Gonzalo Mena (UC Berkeley)\nJoin Zoom Meeting\nhttps://reuna.zoom.us/j/84521834914?pwd=OTZ6Y0NWM3pYTGtTbEt3c0luTG96UT09\nID de reunión: 845 2183 4914\nCódigo de acceso: 997973\n\n
URL:https://www.cmm.uchile.cl/events/chilean-probability-seminar-on-model-based-clustering-with-entropic-optimal-transport/
CATEGORIES:Seminarios
LOCATION:Sala Maryam Mirzakhani - 6to piso CMM
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