[Forum SIS] One World ABC Seminar: "Neural Approximate Sufficient Statistics, " Michael Gutmann - October 28

[Umberto Picchini]

Dear all


it is time again for a new talk at the One WorldABC Seminar 
<https://warwick.ac.uk/fac/sci/statistics/news/upcoming-seminars/abcworldseminar>!

Our next speaker will be Michael Gutmann 
<https://michaelgutmann.github.io>, who will talk about /Neural 
Approximate Sufficient Statistics 
<https://openreview.net/pdf?id=SRDuJssQud>/, onThursday October 28, at 
11.30am UK time, with an abstract reported below.

Coordinates to join the talk are below. However more generally please 
*sign up to the dedicated **email list 
<https://listserv.csv.warwick.ac.uk/mailman/listinfo/abc_world_seminar>***to 
get updates about future One World ABC seminars.

https://ed-ac-uk.zoom.us/j/82567695825 
<https://ed-ac-uk.zoom.us/j/82567695825>
Meeting ID: 825 6769 5825
Passcode: hq1z8WYx

We look forward to seeing you on Thursday,
best wishes,
The One World ABC Seminar Organisers.

---

*When*: Thursday, October 28, 11.30am UK Time
*Speaker*: Michael Gutmann <https://michaelgutmann.github.io>, 
University of Edinburg
*Title*: Neural Approximate Sufficient Statistics
*Abstract*: My talk will first review the concept of sufficient 
statistics and then explain that we can learn them by learning mutual 
information maximizing representations. I will then explain how we used 
the learned statistics to boost the performance of both classical and 
recent methods for Bayesian parameter inference when the likelihood is 
intractable but sampling from the model is possible.

The talk is based on the paper:
Neural Approximate Sufficient Statistics for Implicit Models. Y. Chen, 
D. Zhang, M. Gutmann, A. Courville, and Z. Zhu. In International 
Conference on Learning Representations (ICLR) 2021
https://openreview.net/pdf?id=SRDuJssQud 
<https://openreview.net/pdf?id=SRDuJssQud>

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