---
title: "ICLR 2020 Notes: Speakers and Workshops"
date: 2020-05-05
toc: true
---

ICLR is one of the most important conferences in machine learning, and
as such, I was very excited to have the opportunity to volunteer and
attend the first fully-virtual edition of the event. The whole content
of the conference has been made [[https://iclr.cc/virtual_2020/index.html][publicly available]], only a few days
after the end of the event!

I would like to thank the [[https://iclr.cc/Conferences/2020/Committees][organizing committee]] for this incredible
event, and the possibility to volunteer to help other
participants[fn:volunteer].

The many volunteers, the online-only nature of the event, and the low
registration fees also allowed for what felt like a very diverse,
inclusive event. Many graduate students and researchers from industry
(like me), who do not generally have the time or the resources to
travel to conferences like this, were able to attend, and make the
exchanges richer.

In this post, I will try to give my impressions on the event, the
speakers, and the workshops that I could attend. I will do a quick
recap of the most interesting papers I saw in a future post.

[fn:volunteer] To better organize the event, and help people navigate
the various online tools, they brought in 500(!) volunteers, waved our
registration fees, and asked us to do simple load-testing and tech
support. This was a very generous offer, and felt very rewarding for
us, as we could attend the conference, and give back to the
organization a little bit.

* The Format of the Virtual Conference

As a result of global travel restrictions, the conference was made
fully-virtual. It was supposed to take place in Addis Ababa, Ethiopia,
which is great for people who are often the target of restrictive visa
policies in Northern American countries.

The thing I appreciated most about the conference format was its
emphasis on /asynchronous/ communication. Given how little time they
had to plan the conference, they could have made all poster
presentations via video-conference and call it a day. Instead, each
poster had to record a 5-minute video[fn:slideslive] summarising their
research. Alongside each presentation, there was a dedicated
Rocket.Chat channel[fn:rocketchat] where anyone could ask a question
to the authors, or just show their appreciation for the work. This was
a fantastic idea as it allowed any participant to interact with papers
and authors at any time they please, which is especially important in
a setting where people were spread all over the globe.

There were also Zoom session where authors were available for direct,
face-to-face discussions, allowing for more traditional
conversations. But asking questions on the channel had also the
advantage of keeping a track of all questions that were asked by other
people. As such, I quickly acquired the habit of watching the video,
looking at the chat to see the previous discussions (even if they
happened in the middle of the night in my timezone!), and then
skimming the paper or asking questions myself.

All of these excellent ideas were implemented by an [[https://iclr.cc/virtual_2020/papers.html?filter=keywords][amazing website]],
collecting all papers in a searchable, easy-to-use interface, and even
including a nice [[https://iclr.cc/virtual_2020/paper_vis.html][visualisation]] of papers as a point cloud!

[fn:slideslive] The videos are streamed using [[https://library.slideslive.com/][SlidesLive]], which is a
great solution for synchronising videos and slides. It is very
comfortable to navigate through the slides and synchronising the video
to the slides and vice-versa. As a result, SlidesLive also has a very
nice library of talks, including major conferences. This is much
better than browsing YouTube randomly.

[fn:rocketchat] [[https://rocket.chat/][Rocket.Chat]] seems to be an [[https://github.com/RocketChat/Rocket.Chat][open-source]] alternative to
Slack. Overall, the experience was great, and I appreciate the efforts
of the organizers to use open source software instead of proprietary
applications. I hope other conferences will do the same, and perhaps
even avoid Zoom, because of recent privacy concerns (maybe try
[[https://jitsi.org/][Jitsi]]?).

* Speakers

Overall, there were 8 speakers (two for each day of the main
conference). They made a 40-minute presentation, and then there was a
Q&A both via the chat and via Zoom. I only saw a few of them, but I
expect I will be watching the others in the near future.

** Prof. Leslie Kaelbling, [[https://iclr.cc/virtual_2020/speaker_2.html][Doing for Our Robots What Nature Did For Us]]

This talk was fascinating. It is about robotics, and especially how to
design the "software" of our robots. We want to program a robot in a
way that it could work the best it can over all possible domains it
can encounter. I loved the discussion on how to describe the space of
distributions over domains, from the point of view of the robot
factory:
- The domain could be very narrow (e.g. playing a specific Atari game)
  or very broad and complex (performing a complex task in an open
  world).
- The factory could know in advance in which domain the robot will
  evolve, or have a lot of uncertainty around it.

There are many ways to describe a policy (i.e. the software running in
the robot's head), and many ways to obtain them. If you are familiar
with recent advances in reinforcement learning, this talk is a great
occasion to take a step back, and review the relevant background ideas
from engineering and control theory.

Finally, the most important take-away from this talk is the importance
of /abstractions/. Whatever the methods we use to program our robots,
we still need a lot of human insights to give them good structural
biases. There are many more insights, on the cost of experience,
(hierarchical) planning, learning constraints, etc, so I strongly
encourage you to watch the talk!

** Dr. Laurent Dinh, [[https://iclr.cc/virtual_2020/speaker_4.html][Invertible Models and Normalizing Flows]]

This is a very clear presentation of an area of ML research I do not
know very well. I really like the approach of teaching a set of
methods from a "historical", personal point of view. Laurent Dinh
shows us how he arrived at this topic, what he finds interesting, in a
very personal and relatable manner. This has the double advantage of
introducing us to a topic that he is passionate about, while also
giving us a glimpse of a researcher's process, without hiding the
momentary disillusions and disappointments, but emphasising the great
achievements. Normalizing flows are also very interesting because it
is grounded in strong theoretical results, that brings together a lot
of different methods.

** Profs. Yann LeCun and Yoshua Bengio, [[https://iclr.cc/virtual_2020/speaker_7.html][Reflections from the Turing Award Winners]]

This talk was very interesting, and yet felt very familiar, as if I
already saw a very similar one elsewhere. Especially for Yann LeCun,
who clearly reuses the same slides for many presentations at various
events. They both came back to their favourite subjects:
self-supervised learning for Yann LeCun, and system 1/system 2 for
Yoshua Bengio. All in all, they are very good speakers, and their
presentations are always insightful. Yann LeCun gives a lot of
references on recent technical advances, which is great if you want to
go deeper in the approaches he recommends. Yoshua Bengio is also very
good at broadening the debate around deep learning, and introducing
very important concepts from cognitive science.

# ** Prof. Michael I. Jordan, [[https://iclr.cc/virtual_2020/speaker_8.html][The Decision-Making Side of Machine Learning: Dynamical, Statistical and Economic Perspectives]]

# TODO

* Workshops

On Sunday, there were [[https://iclr.cc/virtual_2020/workshops.html][15 different workshops]]. All of them were
recorded, and are available on the website. As always, unfortunately,
there are too many interesting things to watch everything, but I saw
bits and pieces of different workshops.

** [[https://iclr.cc/virtual_2020/workshops_12.html][Beyond 'tabula rasa' in reinforcement learning: agents that remember, adapt, and generalize]]

A lot of pretty advanced talks about RL. The general theme was
meta-learning, aka "learning to learn". This is a very active area of
research, which goes way beyond classical RL theory, and offer many
interesting avenues to adjacent fields (both inside ML and outside,
especially cognitive science). The [[http://www.betr-rl.ml/2020/abs/101/][first talk]], by Martha White, about
inductive biases, was a very interesting and approachable introduction
to the problems and challenges of the field. There was also a panel
with Jürgen Schmidhuber. We hear a lot about him from the various
controversies, but it's nice to see him talking about research and
future developments in RL.

** [[https://iclr.cc/virtual_2020/workshops_14.html][Causal Learning For Decision Making]]

Ever since I read Judea Pearl's [[https://www.goodreads.com/book/show/36204378-the-book-of-why][/The Book of Why/]] on causality, I have
been interested in how we can incorporate causality reasoning in
machine learning. This is a complex topic, and I'm not sure yet that
it is a complete revolution as Judea Pearl likes to portray it, but it
nevertheless introduces a lot of new fascinating ideas. Yoshua Bengio
gave an interesting talk[fn:bengioworkshop] (even though very similar
to his keynote talk) on causal priors for deep learning.

[fn:bengioworkshop] You can find it at 4:45:20 in the [[https://slideslive.com/38926830/workshop-on-causal-learning-for-decision-making][livestream]] of
the workshop.

** [[https://iclr.cc/virtual_2020/workshops_4.html][Bridging AI and Cognitive Science]]

Cognitive science is fascinating, and I believe that collaboration
between ML practitioners and cognitive scientists will greatly help
advance both fields. I only watched [[https://baicsworkshop.github.io/program/baics_45.html][Leslie Kaelbling's presentation]],
which echoes a lot of things from her talk at the main conference. It
complements it nicely, with more focus on intelligence, especially
/embodied/ intelligence. I think she has the right approach to
relationships between AI and natural science, explicitly listing the
things from her work that would be helpful to natural scientists, and
things she wish she knew about natural intelligences. It raises many
fascinating questions on ourselves, what we build, and what we
understand. I felt it was very motivational!

** [[https://iclr.cc/virtual_2020/workshops_5.html][Integration of Deep Neural Models and Differential Equations]]

I didn't attend this workshop, but I think I will watch the
presentations if I can find the time. I have found the intersection of
differential equations and ML very interesting, ever since the famous
[[https://papers.nips.cc/paper/7892-neural-ordinary-differential-equations][NeurIPS best paper]] on Neural ODEs. I think that such improvements to
ML theory from other fields in mathematics would be extremely
beneficial to a better understanding of the systems we build.
