Connectionists: ICBINB Monthly Seminar Series Talk: Benjamin Bloem-Reddy

[Francisco J. Rodríguez Ruiz]

Dear all,

We are pleased to announce that the next speaker of the *“I Can’t Believe
It’s Not Better!” (**ICBINB)* virtual seminar series will be *Benjamin
Bloem-Reddy** (**University of British Columbia**)*. More details about
this series and the talk are below.

The *"I Can't Believe It's Not Better!" (ICBINB) monthly online seminar
series* seeks to shine a light on the "stuck" phase of research. Speakers
will tell us about their most beautiful ideas that didn't "work", about
when theory didn't match practice, or perhaps just when the going got
tough. These talks will let us peek inside the file drawer of unexpected
results and peer behind the curtain to see the real story of *how real
researchers did real research*.

*When: *August 4th, 2022 at 8am PDT / 11am EDT / 5pm CEST
(*Note*: The time differs from our usual one.)

*Where: *RSVP for the Zoom link here:
https://us02web.zoom.us/meeting/register/tZUtceitrzgvHNYgvD02gj57-kxKNahUdTiC

*Title:* *From Identifiability to Structured Representation Spaces, and a
Case for (Precise) Pragmatism in Machine Learning*

*Abstract: **There has been a recent surge in research activity related to
identifiability in generative models involving latent variables. Why should
we care whether a latent variable model is identifiable? I will give some
pragmatic reasons, which differ philosophically from and which have
different practical and theoretical implications than, classical views on
identifiability, which usually relate to recovering the “true” distribution
or “true” latent factors of variation. In particular, a pragmatic approach
requires us to consider how the structure we are imposing (or not imposing)
on the latent space relates to the problems we’re trying to solve. I will
highlight how I think a lack of precise pragmatism is holding back modern
methods in challenging settings, including how aspects of my own research
on identiability has gotten stuck without problem-specific constraints.
Elaborating on methods for representation learning more generally, I will
discuss some ways we can (and are beginning to) structure our latent spaces
to achieve specific goals other than vague appeals to general AI.*

*Bio:*
*Benjamin Bloem-Reddy is an Assistant Professor of Statistics at the
University of British Columbia. He works on problems in statistics and
machine learning, with an emphasis on probabilistic approaches. He has a
growing interest in causality and its interplay with knowledge and
inference and also collaborates with researchers in the sciences on
statistical problems arising in their research.*
*Bloem-Reddy was a PhD student with Peter Orbanz at Columbia and a postdoc
with Yee Whye Teh in the CSML group at the University of Oxford. Before
moving to statistics and machine learning, he studied physics at Stanford
University and Northwestern University.*

For more information and for ways to get involved, please visit us at
http://icbinb.cc/, Tweet to us @ICBINBWorkhop
<https://twitter.com/ICBINBWorkshop>, or email us at
cant.believe.it.is.not.better at gmail.com.

--
Best wishes,
The ICBINB Organizers
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