Call for Papers
We invite submissions to the 14th International Conference on Learning Representations 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 about the main conference can also be directed to: program-chairs@iclr.cc
Please see the author guide for all submission instructions and policies.
Key dates
The planned dates are as follows (all times are UTC-12h, aka “Anywhere on Earth”):
- Abstract submission: 11:59pm, Sep 19
- Submission date: 11:59pm, Sep 24
- Reviews released: Nov 11
- Author/Reviewer Discussion: Nov 11-Dec 3
- Author Last Day to Reply: Dec 3
- Final decisions: Jan 22 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
- 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 guide.
We use OpenReview to host papers and allow for public review and discussion. The program will include oral presentations and posters of accepted papers.