By ICLR 2021 Senior Program Chair Katja Hofmann (Microsoft Research Cambridge) and Program Co-Chairs Naila Murray (Facebook), Alice Oh (KAIST), Ivan Titov (U. Edinburgh)

Acceptance notifications for ICLR 2021 were released on January 12. Out of the 2997 full submissions that were received, a total of 860 have been accepted for presentation at the conference. Our heartfelt congratulations to all authors! We thank all authors for submitting their work to ICLR, and hope that even if your work was not accepted at this time, you will consider joining the exciting conference ahead.

The accepted contributions will make up an exciting…

By ICLR 2021 Senior Program Chair Katja Hofmann (Microsoft Research), Program Co-Chairs Ivan Titov (U. Edinburgh / U. Amsterdam), Naila Murray (Facebook AI Research), and Alice Oh (KAIST), and Virtual Chairs Hendrik Strobelt (IBM), Matthias Gallé (Naver Labs Europe), Sileye Ba (Loreal), and Marija Stanojevic (Temple University)

The countdown has started — we’re less than 1 week away from kicking off ICLR 2021!

On our side, we cannot wait to welcome you to this year’s virtual conference. The program will be packed with inspiring talks, Q&As, and plenty of opportunity for discussion. …

By ICLR 2021 Program Co-Chairs Ivan Titov (U. Edinburgh / U. Amsterdam), Naila Murray (Facebook AI Research), and Alice Oh (KAIST), and Senior Program Chair Katja Hofmann (Microsoft Research)

We are thrilled to announce the winners of the ICLR 2021 Outstanding Paper Awards. While there are 860 excellent papers in our program, and many of them of exceptional quality, we would like to highlight 8 papers that are especially noteworthy.

Award winners (in alphabetical order):

By ICLR 2021 Senior Program Chair Katja Hofmann (Microsoft Research, Cambridge, UK) and Program Co-Chairs Naila Murray (Facebook AI Research), Alice Oh (KAIST), Ivan Titov (U. Edinburgh)

It’s time for a big drum-roll … today we are announcing our invited speakers for ICLR 2021! Much activity precedes this exciting moment. First, we collect a long list of candidate speakers, many of whom are suggestions from the community, and especially our ICLR Area Chairs. Second, we in the program committee consider all suggestions to take into account how each would contribute to widening the range of perspectives at the event. …

Congratulations to all authors who successfully submitted to ICLR 2021! With a major crisis on our submission deadline, on top of the months-long pandemic, amid the stressful state of the world especially for our American colleagues, authors have contributed more than 3,000 final submissions, and they are available for your perusal on Openreview. As announced earlier on our website, ICLR 2021 will be fully virtual from May 4th to 8th, 2021, and while we are disappointed about not gathering in Austria, the virtual format offers access to a wider audience, and so we are looking forward to another great conference.

From today, all content for the ICLR2020 Virtual Conference is available in open-access for anyone across the world to learn from. A public archive of the virtual conference site is now available for everyone to explore the 2020 conference proceedings, and to get a sense of the virtual conference portal and its flow. The registered participants site remains available.

Organising the 8th international conference on learning representations (ICLR 2020) was highly challenging, but ultimately, highly rewarding for our organising committees. Our initial work for hosting the conference in Ethiopia was far along, but then COVID-19 pandemic gave us the opportunity…

ICLR2020 begins this weekend! Starting today, the conference portal can be accessed, to help everyone registered prepare for the week ahead. We hope everyone will learn new things, meet new people, and have a productive and fun virtual conference.

To make the conference fit the culture and needs of the ICLR community, we built our own virtual portal that gives you all the elements of the conference that you expect. Access is restricted to registered participants. You can still register at if you want to join us.

Registering allows you participate in all the live components of the conference:

ICLR2020 is going to be fully virtual this year, and so too are the ICLR workshops. Our changing conference format creates a great deal of uncertainty and pressure on workshop organisers, and ICLR is doing all we can to support workshops that are fun, create a long-lasting archive of their activities, and can explore different ways of creating virtual workshops that can provide insight into the future of such events across our field.

👷 The 2020 Workshops

We’re confident that we will be able to provide a useful, meaningful, and intellectually stimulating workshop experience, even though we will not be physically co-located. …

Last December we announced all the papers that were accepted and will be presented at the 2020 International Conference on Learning Representations (ICLR). Much has changed since then with the conference now being hosted in a fully virtual format, but all the great content remains, especially the collection of excellent speakers and talks.

The collection of accepted papers formed #OurHatata: Our inquiry into the principles and practice of machine learning and artificial intelligence. All accepted papers will be presented as videos with live video poster sessions. These presenters are joined by a set of incredible keynote speakers, who will amplify…

ICLR2020 will be hosted as a fully virtual conference this year. This is an exciting opportunity to experiment with the way we organise our conference and we hope that as many people from our machine learning community will participate in this year’s meeting.

Our Hatata goes virtual, and this post gives an overview of the format, the resources we will be using, and a summary of the deliberations that took place amongst the ICLR organising committees, which we hope can be a basis for future improvements. The description below is how we expect the virtual conference to work, although there…


International Conference on Learning Representations

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