Conference Information

ICLR 2027: International Conference on Learning Representations

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Submission Date:
2026-09-18 Due in 10 days
Notification Date:
2026-12-16
Conference Date:
2027-04-26
Location:
San Francisco, California, USA
Years:
CCF: A   ICORE: A*   Viewed: 160187   Tracked: 284   Attend: 19

Conference Partner Index (CP-I)

89.1 / 100
Ranked #81 of 5,652 conferences · Top 2%

#9 of 736 in Artificial Intelligence & Machine Learning

Academic recognition (35%)
100
Submission selectivity (20%)
77
Editions held (20%)
75
Community attention (10%)
88
Public record completeness (15%)
100

Inputs used: Listed as CCF A · Acceptance rate: 31.2% (mean of 5 editions on file) · Editions on record: 15 · Researchers following it here: 284 · Researchers who opened this page in the past 24 months: 88

Confidence 100% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-08

Call For Papers

ICLR 2027 (International Conference on Learning Representations) is a CCF A / ICORE A* conference held in San Francisco, California, USA on 2027-04-26. The paper submission deadline is 2026-09-18. Acceptance notifications are sent on 2026-12-16.

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.
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Acceptance Ratio

Average acceptance rate: 31.7% over 9 years (2017–2025).

YearSubmittedAcceptedAccepted(%)
202511565370832.1%
20247304225030.8%
20234956157431.8%
20223328109532.9%
2021299786028.7%
2020259468726.5%
2019159150031.4%
201898131432%
201750719839.1%

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