{"675266":{"#nid":"675266","#data":{"type":"event","title":"PhD Defense by Jiaojiao Fan","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle: Learning and inference for distributions: from optimal transport to MCMC\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EDate: Friday, July 5th, 2024\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETime: 9:30 am - 12:00 pm EST (9:30 pm - 12:00 am\u0026nbsp;Hong Kong Time)\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003ELocation: Zoom meeting \u003Ca href=\u0022https:\/\/gatech.zoom.us\/j\/91660072023\u0022\u003Ehttps:\/\/gatech.zoom.us\/j\/91660072023\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EJiaojiao Fan\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EMachine Learning PhD Student\u003C\/p\u003E\u003Cp\u003ESchool of Aerospace Engineering\u003Cbr\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E1 Prof. Yongxin Chen (School of Aerospace Engineering, Georgia Tech; Advisor)\u003C\/p\u003E\u003Cp\u003E2 Prof. Molei Tao (School of Mathematics, Georgia Tech)\u003C\/p\u003E\u003Cp\u003E3 Prof. Peng Chen (School of Computational Science and Engineering, Georgia Tech)\u003C\/p\u003E\u003Cp\u003E4 Prof. Haomin\u0026nbsp;Zhou (School of Mathematics, Georgia Tech)\u003C\/p\u003E\u003Cp\u003E5 Prof. Jiaming Liang (Goergen Institute for Data Science \u0026amp; Department of Computer Science, University of Rochester)\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EIn machine learning, distributional data are ubiquitous\u0026nbsp;and pivotal across diverse fields. This dissertation tackles large-scale challenges associated with distributional data, which are often defined by either the volume of data or their high dimensionality, particularly in learning and inference contexts.\u003C\/p\u003E\u003Cp\u003EWe explore two fundamental mathematical tools essential for the transformation and manipulation of distributional data: optimal transport (OT) and Markov Chain Monte Carlo (MCMC) sampling. OT, a centuries-old mathematical framework, is used for comparing probability distributions but is often hindered by high computational costs. We enhance the computational efficiency of OT by focusing on two aspects: improving multi-marginal OT with graph structures, and scaling OT to handle millions of samples through the introduction of neural OT solvers.\u003C\/p\u003E\u003Cp\u003EMCMC sampling, while being the primary method for drawing samples from a given probability density, faces scalability issues, especially in higher dimensions. To overcome these limitations, we introduce a novel algorithm based on the proximal sampler, a type of Gibbs sampling method, and rigorously demonstrate its superior computational efficiency in converging to the target distribution.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ESee Below\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Learning and inference for distributions: from optimal transport to MCMC"}],"uid":"27707","created_gmt":"2024-06-28 14:34:29","changed_gmt":"2024-06-28 14:34:29","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-07-05T09:30:57-04:00","event_time_end":"2024-07-05T12:00:00-04:00","event_time_end_last":"2024-07-05T12:00:00-04:00","gmt_time_start":"2024-07-05 13:30:57","gmt_time_end":"2024-07-05 16:00:00","gmt_time_end_last":"2024-07-05 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Zoom meeting ","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}