The Tenth International Conference on Learning Representations (Virtual)
Mon Apr 25th through Fri the 29th


Sponsors

The generous support of our sponsors allowed us to reduce our ticket price by about 50%, and support diversity at the meeting with travel awards. In addition, many accepted papers at the conference were contributed by our sponsors.

View ICLR 2022 sponsors »Become a 2023 Sponsor »(not currently taking applications)

 

2022 ICLR Organizing Committee

General Chair

  • Katja Hofmann, Microsoft
  • Deputy GC: Alexander (Sasha) Rush, Cornell Tech

Senior Program Chair

  • Yan Liu, University of Southern California

Program Chairs

  • Chelsea Finn, Stanford University
  • Yejin Choi, University of Washington / AI2
  • Marc Deisenroth, University College London

Workshop Chairs

  • Feryal Behbahani, DeepMind
  • Vukosi Marivate, University of Pretoria

Area Chairs

Ethics Review Committee

  • Thomas G. Dietterich, Oregon State University
  • Ayanna Howard, Ohio State University 
  • Patrick Lin, California Polytechnic State University
  • Miguel Luengo-Oroz, UN Global Pulse
  • Shannon Vallor, University of Edinburgh
  • Robert Sparrow, Monash University

Diversity Equity & Inclusion Chairs

  • Krystal Maughan, University of Vermont
  • Rosanne Liu, Google & ML Collective

Virtual Chairs - Virtual & Volunteers 

  • Jumanah Alshehri, Temple University
  • Archana David, Infosys Ltd

Engagements Chairs - Socials & Sponsors

  • Ehi Nosakhare, Microsoft
  • William Agnew, University of Washington

Blog Track Chairs

  • Sebastien Bubeck, Microsoft
  • David Dobre, MILA
  • Charlie Gauthier, MILA
  • Gauthier Gidel, MILA
  • Claire Vernade, DeepMind

Workflow Chairs

  • Zhenyu (Sherry) Xue
  • Yaguang Li, Google Brain

Contact

The organizers can be contacted here.

About Us

The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning.

ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, text understanding, gaming, and robotics.

Participants at ICLR span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

A non-exhaustive list of relevant topics explored at the conference include:

  • unsupervised, semi-supervised, and supervised representation learning
  • representation learning for planning and reinforcement learning
  • representation learning for computer vision and natural language processing
  • metric learning and kernel learning
  • sparse coding and dimensionality expansion
  • hierarchical models
  • optimization for representation learning
  • learning representations of outputs or states
  • optimal transport
  • theoretical issues in deep learning
  • societal considerations of representation learning including fairness, safety, privacy, and interpretability, and explainability
  • visualization or interpretation of learned representations
  • implementation issues, parallelization, software platforms, hardware
  • climate, sustainability
  • applications in audio, speech, robotics, neuroscience,  biology, or any other field