Top Computer Science & AI Conferences (2026–2027): CORE A* Rankings, Acceptance Rates & Strategies
A comprehensive analysis of CORE A* and A ranked conferences in AI, Machine Learning, Computer Vision, and Systems. Review acceptance rates, publication cycles, and targeting strategies.
Executive Summary & Key Takeaways
- CORE A* conferences represent the top 4-7% of global Computer Science venues with international acclaim.
- Flagship AI conferences (NeurIPS, ICML, ICLR, CVPR) maintain rigorous 18% to 26% acceptance rates.
- Plan paper development across annual submission cycles (e.g. CVPR in Nov, ICML in Jan, NeurIPS in May, ICLR in Sept).
- Align benchmark empirical results with rigorous error bounds and ablation studies to score 6+ (Accept) on OpenReview.
Understanding CORE Conference Rankings
The Computing Research and Education Association of Australasia (CORE) portal administers one of the most widely recognized quality assessment rankings for international Computer Science conferences:
- CORE A*: Flagship conferences of the highest standing, typically representing the top 4% to 7% of venues in the discipline.
- CORE A: Excellent international conferences with high impact and rigorous peer-review (15% to 20% of venues).
- CORE B: Good quality international conferences with solid community participation.
- CORE C: Specialized or regional conferences.
Tier-1 AI & Computer Science Conferences Overview
| Conference | Domain / Track | Typical Acceptance Rate | Submission Window | Primary Review System |
|---|---|---|---|---|
| NeurIPS | Machine Learning & Neural Computation | ~21% - 25% | May (Annual) | OpenReview |
| ICML | International Conference on Machine Learning | ~20% - 24% | January (Annual) | OpenReview |
| ICLR | International Conference on Learning Representations | ~28% - 31% | September (Annual) | OpenReview |
| CVPR | Computer Vision & Pattern Recognition | ~22% - 26% | November (Annual) | Microsoft CMT |
| ICCV / ECCV | Computer Vision Biennial Flagships | ~20% - 25% | March / July (Biennial) | Microsoft CMT |
| ACL | Association for Computational Linguistics (NLP) | ~20% - 23% | ARR (Rolling / Monthly) | OpenReview / ARR |
| KDD | Knowledge Discovery & Data Mining | ~15% - 20% | February / August | Microsoft CMT |
| SIGCOMM | Computer Systems & Networking | ~15% - 18% | January (Annual) | HotCRP |
| SOSP / OSDI | Operating Systems & Distributed Systems | ~14% - 19% | Spring / Autumn | HotCRP |
| IEEE S&P (Oakland) | Security & Cryptography | ~12% - 15% | Multi-cycle (4x / year) | Custom Portal |
Key Strategies for Publishing in CORE A* Venues
1. Robust Theoretical Foundations + Empirical Ablations
In premier AI/ML venues, pure empirical gains on standard benchmarks (e.g. ImageNet, GLUE, MMLU) are no longer sufficient. High-scoring reviews demand:
- Theoretical justification: Why does the proposed formulation converge or generalize better?
- Ablation Studies: Isolated component-by-component validation demonstrating which hyperparameter or layer drives the improvement.
- Reproducibility: Complete pseudo-code, compute budget declarations, and open-source GitHub release repository.
2. Multi-Cycle Pipeline Strategy
Experienced research laboratories run continuous pipelines synced with global conference deadlines:
- January: Submit to ICML.
- May: Submit new or refined work to NeurIPS.
- September: Target ICLR or AAAI.
- November: Submit computer vision work to CVPR.
Verified Computer Science & AI Conferences & Deadlines
- IMS RFSA — 2029 IEEE MTT-S Radio Frequency Systems and Applications Symposium (IMS RFSA) (2029-06-10 • Washington, USA)
- IMS RFSA — 2027 IEEE MTT-S Radio Frequency Systems and Applications Symposium (IMS RFSA) (2027-05-23 • San Antonio, USA)
- SSCI — 2027 IEEE Symposium Series on Computational Intelligence (SSCI) (2027-02-14 • Gold Coast, Australia)
- IMS RFTT — 2027 IEEE MTT-S Radio Frequency Technology and Techniques Symposium (IMS RFTT) (2027-05-23 • San Antonio, USA)
Indexed Journals in Computer Science & AI
- IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI) • Impact Factor: 20.8
- Nature Machine Intelligence (Nat Mach Intell) • Impact Factor: 18.8
- IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS) • Impact Factor: 10.4