Next week, we have the pleasure of having Prof. Elchanan Mossel give a talk in the colloquium.

The seminar will be held on Monday, May 20th at 14:00.

Location: C220.

The title, abstract and bio appear below.

Looking forward to seeing you,

Sagie and Liat

Why depth? Some new perspectives on the advantages of depth in inference

Can theory help explain the success of deep nets on real data? One avenue to explore this question is to ask if we can find

1. Natural data models where:
2. Inference is computationally and statistically efficient,
3. Inference requires depth (or some other measure of complexity) and
4. The inference procedure can be learned efficiently from data

As proving lower bounds in theoretical computer science for explicit objects is hard, perhaps the most difficult task is to establish 3.
I will discuss some recent works that try to establish 1-4 for the broadcast model on the tree and where the inference procedure is belief propagation.

Based on:

https://arxiv.org/pdf/2402.13359
https://dl.acm.org/doi/abs/10.1145/3564246.3585155
https://proceedings.neurips.cc/paper_files/paper/2022/hash/77e6814d32a86b76123bd10aa7e2ad81-Abstract-Conference.html
https://proceedings.mlr.press/v125/moitra20a.html
https://arxiv.org/pdf/1612.09057

Bio:

Elchanan Mossel is a professor of mathematics at the Massachusetts Institute of Technology. His primary research fields are probability theory, combinatorics, and statistical inference.