[Sds-seminars] S&DS In-Person Seminar, Jiaoyang Huang, 12/19, 4pm, "Efficient derivative-free Bayesian inference for large-scale inverse problems"

[Fan, Zhou]

Hi all,

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".

-Zhou
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Subject: [Sds-seminars] S&DS In-Person Seminar, Jiaoyang Huang, 12/19, 4pm, "Efficient derivative-free Bayesian inference for large-scale inverse problems"


[Department of Statistics and Data Science]<https://statistics.yale.edu/>   Department of Statistics and Data Science <https://statistics.yale.edu/>

In-Person seminars will be held at Dunham Lab, 10 Hillhouse Ave., Room 220, with an option of remote participation via zoom.

3:30pm -   Pre-talk meet and greet, DL Suite 222, Room 228



JIAOYANG HUANG, University of Pennsylvania

[cid:image004.jpg at 01D90FB9.2C843AD0]

Date: Monday, December 19, 2022

Time: 4:00PM to 5:00PM

Location: Dunham Lab. see map<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>

10 Hillhouse Avenue, Rm. 220

New Haven, CT 06511

Website<https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fstatistics.wharton.upenn.edu%2Fprofile%2Fhuangjy%2F&data=05%7C01%7Czf59%40connect.yale.edu%7C6e435ed353594e75494d08daddfa405b%7Cdd8cbebb21394df8b4114e3e87abeb5c%7C0%7C0%7C638066364174558238%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=1k2z4EPqb86xPUjAasLW%2BLH%2Brhy%2FowJ0%2BPuoNr%2FzZw0%3D&reserved=0>







Title: Efficient derivative-free Bayesian inference for large-scale inverse problems



Information and Abstract:

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.

This is based on joint works with Yifan Chen, Daniel Zhengyu Huang, Sebastian Reich and Andrew Stuart.

Zoom Option: Link: Join from PC, Mac, Linux, iOS or Android: https://yale.zoom.us/j/92411077917?pwd=aXhnTnFGRXFoaTVDczNjeFFKeWpTQT09

Password: 24

Or Telephone：203-432-9666 (2-ZOOM if on-campus) or 646 568 7788

Meeting ID: 924 1107 7917











Department of Statistics and Data Science

Yale University
24 Hillhouse Avenue
New Haven, CT 06511

t 203.432.0666
f 203.432.0633

For more details and upcoming events visit our website at http://statistics.yale.edu/<https://nam12.safelinks.protection.outlook.com/?url=http%3A%2F%2Fstatistics.yale.edu%2F&data=05%7C01%7Czf59%40connect.yale.edu%7C6e435ed353594e75494d08daddfa405b%7Cdd8cbebb21394df8b4114e3e87abeb5c%7C0%7C0%7C638066364174558238%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=wwIhGsrpWpWwe05HvTCoiCV4kftmsULS0Qj2pKLry%2F0%3D&reserved=0>


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