- Authors of eligible TMLR publications can submit a request to present in ICLR conference! Find more here.
- Self-nomination form for ICLR 2024 Reviewing. Interested in being a reviewer? Fill out the form!
- BEWARE of Predatory ICLR conferences being promoted through the World Academy of Science, Engineering and Technology organization.
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 2024 Sponsor »
2022 ICLR Organizing Committee
- Katja Hofmann, Microsoft
- Deputy GC: Alexander (Sasha) Rush, Cornell Tech
Senior Program Chair
- Yan Liu, University of Southern California
- Chelsea Finn, Stanford University
- Yejin Choi, University of Washington / AI2
- Marc Deisenroth, University College London
- Feryal Behbahani, DeepMind
- Vukosi Marivate, University of Pretoria
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
- Zhenyu (Sherry) Xue, ICLR
- Yaguang Li, Google Brain
The organizers can be contacted here.
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