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ICLR 2027 Call for Papers

We invite submissions to the 15th International Conference on Learning Representations, and welcome paper submissions from all areas of machine learning.

For any information needed that is not listed below, please submit questions using this link: Contact ICLR Program Chairs.

Questions can be directed to: program-chairs@iclr.cc.

Please see the author guidelines for all submission instructions and policies. Please read these instructions carefully, as we have new policies this year regarding co-authorship, reciprocal reviewing, and the use of AI.

Key dates

The planned dates are as follows (all times are UTC-12h, aka “Anywhere on Earth”):

Abstract deadline Sep 18, 2026 AOE

Paper deadline Sep 25, 2026 AOE

Reviews released Nov 05, 2026

Author-reviewer discussion Nov 05, 2026–Nov 18, 2026

Reviewer-AC discussion Nov 19, 2026–Dec 16, 2026

Final decisions: Dec 16, 2026

Subject Areas

We consider a broad range of subject areas including feature learning, metric learning, compositional modeling, structured prediction, reinforcement learning, uncertainty quantification and issues regarding large-scale learning and non-convex optimization, as well as applications in vision, audio, speech, language, music, robotics, games, healthcare, biology, sustainability, economics, ethical considerations in ML, and others.

A non-exhaustive list of relevant topics:

  • unsupervised, self-supervised, semi-supervised, and supervised representation learning
  • transfer learning, meta learning, and lifelong learning
  • reinforcement learning
  • representation learning for computer vision, audio, language, and other modalities
  • metric learning, kernel learning, and sparse coding
  • probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)
  • generative models
  • causal reasoning
  • optimization
  • learning theory
  • learning on graphs and other geometries & topologies
  • societal considerations including fairness, safety, privacy
  • visualization or interpretation of learned representations
  • datasets and benchmarks
  • infrastructure, software libraries, hardware, etc.
  • neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)
  • applications to robotics, autonomy, planning
  • applications to neuroscience & cognitive science
  • applications to physical sciences (physics, chemistry, biology, etc.)
  • general machine learning (i.e., none of the above)
     

Submissions will be double blind: reviewers cannot see author names when conducting reviews, and authors cannot see reviewer names.  Having papers on arxiv is allowed per the dual submission policy outlined in the author guidelines.

 

Note for first-time submitters

We are excited that improved AI tools are expanding the community of researchers, and we encourage submissions from authors new to ICLR! However, we also feel that it is important to emphasize that the ICLR peer review process involves an enormous amount of volunteer work by thousands of people, and we ask that you respect their time. A typical ICLR paper involves significantly more work than goes into a course project, or than can be produced autonomously by any current AI agent. Because of the open nature of ICLR’s review process, all submissions (even rejected and withdrawn submissions) will remain public with authors’ names attached to them. If you are not sure your submission represents an appropriate amount of work for ICLR, —or if you’re not completely certain that everything in your paper is correct—please keep working rather than submitting now. We’ll still be here next year and look forward to receiving your work then!
Please also be sure to read the author guidelines carefully (particularly the quota section).

 

Note for veteran submitters

Like many of you, the ICLR leadership has been amazed to see the growth in the volume of high-quality ML research in recent years—facilitated not just by coding assistants, but also major advances in theory and research infrastructure, and increasingly close collaboration and integration with scientific and humanistic fields outside the historical domain of “core ML”. We encourage you to take this opportunity to be ambitious! Rather than submitting the same kind of project you might have submitted five years ago (and which you can now, perhaps, complete in half the time), what we want most is your “slow science”, and your most complete and most exciting work.