Connectionists: ICBINB Monthly Seminar Series Talk: Anna Korba

[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 *Anna Korba**
(**ENSAE/CREST**)*. 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: *May 5th, 2022 at 10am EDT / 4pm CEST

*Where: *RSVP for the Zoom link here:
https://us02web.zoom.us/meeting/register/tZMscuGsrD4sHtSIz33Nj44bzC5cpZ4FJmAq

*Title:* *Limitations of the theory for sampling with kernelised
Wasserstein gradient flows*

*Abstract:*
*Sampling from a probability distribution whose density is only known up to
a normalisation constant is a fundamental problem in statistics and machine
learning. Recently, several algorithms based on interactive particle
systems were proposed for this task, as an alternative to Markov Chain
Monte Carlo methods or Variational Inference.These particle systems can be
designed by adopting an optimisation point of view for the sampling
problem: an optimisation objective is chosen (which typically measures the
dissimilarity to the target distribution), and its Wasserstein gradient
flow is approximated by an interacting particle system, which can involve
kernels. At stationarity, the stationarity states of these particle systems
define an empirical measure approximating the target distribution.*

*In this talk I will present recent work on such algorithms, such as Stein
Variational Gradient Descent or Kernel Stein Discrepancy Descent. I will
discuss some recent results that highlight bottlenecks and open questions:
on the empirical side, these particle systems may suffer from convergence
issues, while on the theoretical side, optimisation tools may not be
sufficient to analyse these algorithms. Still, I will also discuss recent
empirical results that show that there is hope in demonstrating nice
approximation properties of these particle systems.*
*Bio:* *Since September 2020, Anna is an assistant professor at ENSAE/
CREST in the Statistics Department. Her main line of research is in
statistical machine learning. She has been working on kernel methods,
optimal transport and ranking data. Currently, she is particularly
interested in dynamical particle systems for ML and kernel-based methods
for causal inference.*

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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