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Hi all,</div>
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A reminder that the S&DS seminar will start in a few minutes (at 4PM) in DL 220. Jiaoyang Huang will speak on "Efficient derivative-free Bayesian inference for large-scale inverse problems".</div>
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-Zhou</div>
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<div id="divRplyFwdMsg" dir="ltr"><font face="Calibri, sans-serif" style="font-size:11pt" color="#000000"><b>From:</b> Sds-seminars <sds-seminars-bounces@mailman.yale.edu> on behalf of elizavette.torres@yale.edu <elizavette.torres@yale.edu><br>
<b>Sent:</b> Wednesday, December 14, 2022 12:39 PM<br>
<b>To:</b> sds-majors@mailman.yale.edu <sds-majors@mailman.yale.edu>; sds-seminars@mailman.yale.edu <sds-seminars@mailman.yale.edu><br>
<b>Subject:</b> [Sds-seminars] S&DS In-Person Seminar, Jiaoyang Huang, 12/19, 4pm, "Efficient derivative-free Bayesian inference for large-scale inverse problems"</font>
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<p class="x_MsoNormal" style="background:white"><span style="color:black"><a href="https://statistics.yale.edu/" title="Department of Statistics and Data Science
"><span style="font-size:22.0pt; font-family:"Lucida Sans",sans-serif; color:#286DC0; text-decoration:none"><img border="0" width="150" height="49" id="x_logo" alt="Department of Statistics and Data Science
" style="width:1.5625in; height:.5069in" data-outlook-trace="F:1|T:1" src="cid:image001.jpg@01D90FB6.F86F79A0"></span></a>
<a href="https://statistics.yale.edu/" title="Home"><b><span style="font-size:22.0pt; font-family:"Lucida Sans",sans-serif; color:#286DC0">Department of Statistics and Data Science </span></b></a></span><b><i><u><span style="font-size:22.0pt; font-family:"Lucida Sans",sans-serif; color:#286DC0">
</span></u></i></b></p>
<p class="x_MsoNormal"><i><span style="font-size:14.0pt">In-Person seminars will be held at Dunham Lab, 10 Hillhouse Ave., Room 220, with an option of remote participation via zoom.</span></i><i><span style="font-size:14.0pt"></span></i></p>
<h1 style="margin:0in"><i><span style="font-size:12.0pt; font-family:"Arial",sans-serif; color:black; font-weight:normal"><a href=""><span style="color:black; text-decoration:none">3:30pm</span></a> - Pre-talk meet and greet, DL Suite 222, Room 228</span></i><span style="font-size:12.0pt; font-family:"Arial",sans-serif; color:black; font-weight:normal"></span></h1>
<p class="x_MsoNormal"><span class="x_date-display-single"><span style="font-size:12.0pt; font-family:"Arial",sans-serif"> </span></span></p>
<p class="x_MsoNormal"><span class="x_date-display-single"><span style="font-size:12.0pt; font-family:"Arial",sans-serif">JIAOYANG HUANG, University of Pennsylvania</span></span><span style="font-size:12.0pt; font-family:"Arial",sans-serif"></span></p>
<p class="x_MsoNormal"><img width="135" height="162" align="left" hspace="12" style="width:1.4027in; height:1.6875in" data-outlook-trace="F:1|T:1" src="cid:image004.jpg@01D90FB9.2C843AD0"><span style="font-size:12.0pt; font-family:"Arial",sans-serif"></span></p>
<p class="x_MsoNormal"><span class="x_date-display-single"><span style="font-size:12.0pt; font-family:"Arial",sans-serif">Date: Monday, December 19, 2022</span></span></p>
<p class="x_MsoNormal"><span class="x_date-display-single"><span style="font-size:12.0pt; font-family:"Arial",sans-serif">Time:
</span></span><span class="x_date-display-start"><span style="font-size:12.0pt; font-family:"Arial",sans-serif">4:00PM</span></span><span class="x_date-display-range"><span style="font-size:12.0pt; font-family:"Arial",sans-serif"> to </span></span><span class="x_date-display-end"><span style="font-size:12.0pt; font-family:"Arial",sans-serif">5:00PM</span></span><span style="font-size:12.0pt; font-family:"Arial",sans-serif"></span></p>
<p class="x_MsoNormal"><span class="x_fn"><span style="font-size:12.0pt; font-family:"Arial",sans-serif">Location: Dunham Lab. </span></span><span class="x_map-icon"><span style="font-size:12.0pt; font-family:"Arial",sans-serif; letter-spacing:.6pt"><a href="https://nam12.safelinks.protection.outlook.com/?url=http%3A%2F%2Fmaps.google.com%2F%3Fq%3D10%2BHillhouse%2BAvenue%252C%2BRm.%2B220%252C%2BNew%2BHaven%252C%2BCT%252C%2B06511%252C%2Bus&data=05%7C01%7Czf59%40connect.yale.edu%7C6e435ed353594e75494d08daddfa405b%7Cdd8cbebb21394df8b4114e3e87abeb5c%7C0%7C0%7C638066364174558238%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=xYJ7PpaYEUgRjj3TH4Cy1Ku%2BIWkknL6x8Rwh9La%2BNcA%3D&reserved=0" originalsrc="http://maps.google.com/?q=10+Hillhouse+Avenue%2C+Rm.+220%2C+New+Haven%2C+CT%2C+06511%2C+us" shash="JO7Xl/yZDqYbgfmUSeLsEktVrBcaP+1R1sT7Anah7sLdHDs9ase7VI66+OAeJLFY7D7h8nYthziabQvtYzpsXbNMwqJkRS5urH3srwSNyP7euWUiK2hR1dbnw3ZLwKVeOTyhcB1JT3+1zrd6J4wjb0RGcHwYzYcnQ9moSqGTWYY="><span style="color:windowtext">see
map</span></a> </span></span><span style="font-size:12.0pt; font-family:"Arial",sans-serif"></span></p>
<p class="x_MsoNormal"><span style="font-size:12.0pt; font-family:"Arial",sans-serif">10 Hillhouse Avenue, Rm. 220</span></p>
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<span style="font-size:12.0pt; font-family:"Arial",sans-serif"></span></p>
<p class="x_MsoNormal"><b><span style="font-size:12.0pt; font-family:"Arial",sans-serif"> </span></b></p>
<p class="x_MsoNormal"><b><span style="font-size:12.0pt; font-family:"Arial",sans-serif"> </span></b></p>
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<p class="x_MsoNormal"><b><span style="font-size:12.0pt; font-family:"Arial",sans-serif">Title: Efficient derivative-free Bayesian inference for large-scale inverse problems</span></b></p>
<p class="x_MsoNormal"><b><span style="font-size:12.0pt; font-family:"Arial",sans-serif"> </span></b></p>
<p class="x_MsoNormal"><b><span style="font-size:12.0pt; font-family:"Arial",sans-serif">Information and Abstract: </span></b></p>
<p style="margin-right:0in; margin-bottom:12.0pt; margin-left:0in; box-sizing:inherit">
<span style="font-size:12.0pt; font-family:"Arial",sans-serif">We consider Bayesian inference for large-scale inverse problems, where computational challenges arise from the need for the repeated evaluations of an expensive forward model, which is often given
as a black box or is impractical to differentiate. In this talk I will propose a new derivative-free algorithm Unscented Kalman Inversion, which utilizes the ideas from Kalman filter, to efficiently solve these inverse problems. First, I will explain some
basics about Variational Inference under general metric tensors. In particular, under the Fisher-Rao metric, the Gaussian Variational Inference leads to the natural gradient descent. Next, I will discuss two different views of our algorithm. It can be obtained
from a Gaussian approximation of the filtering distribution of a novel mean field dynamical system. And it can also be viewed as a derivative-free approximation of the natural gradient descent. I will also discuss theoretical properties for linear inverse
problems. Finally, I will discuss an extension of our algorithm using Gaussian mixture approximation, which leads to the Gaussian Mixture Kalman Inversion, an efficient derivative-free Bayesian inference approach capable of capturing multiple modes. I will
demonstrate the effectiveness of this approach in several numerical experiments with multimodal posterior distributions, which typically converge within O(10) iterations.
</span></p>
<p style="margin-right:0in; margin-bottom:12.0pt; margin-left:0in"><span style="font-size:12.0pt; font-family:"Arial",sans-serif">This is based on joint works with Yifan Chen, Daniel Zhengyu Huang, Sebastian Reich and Andrew Stuart.</span></p>
<p class="x_MsoNormal"><em><b><span style="font-size:12.0pt; font-family:"Arial",sans-serif">Zoom Option:
</span></b></em><span style="font-size:12.0pt; font-family:"Arial",sans-serif">Link: Join from PC, Mac, Linux, iOS or Android: <a href="https://yale.zoom.us/j/92411077917?"><span style="color:#286DC0">https://yale.zoom.us/j/92411077917?</span></a>pwd=aXhnTnFGRXFoaTVDczNjeFFKeWpTQT09 </span></p>
<p class="x_MsoNormal"><span style="font-size:12.0pt; font-family:"Arial",sans-serif">Password: 24</span></p>
<p class="x_MsoNormal"><span style="font-size:12.0pt; font-family:"Arial",sans-serif">Or Telephone</span><span style="font-size:12.0pt; font-family:"MS Gothic"">：</span><span style="font-size:12.0pt; font-family:"Arial",sans-serif">203-432-9666 (2-ZOOM if on-campus)
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<p class="x_MsoNormal"><span style="font-size:12.0pt; font-family:"Arial",sans-serif">Meeting ID: 924 1107 7917</span></p>
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<h2 style="margin:0in; box-sizing:inherit; font-feature-settings:"kern","liga","dlig"">
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<p style="margin:0in; box-sizing:inherit"><span style="font-size:9.0pt; color:black">Yale University<br>
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