Conference Information

ACML 2026: Asian Conference on Machine Learning

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Submission Date:
2026-07-05 Extended
Notification Date:
2026-09-15
Conference Date:
2026-12-01
Location:
Melbourne, Australia
Years:
CCF: C   ICORE: C   Viewed: 167998   Tracked: 303   Attend: 64

Conference Partner Index (CP-I)

76.2 / 100
Ranked #243 of 5,682 conferences · Top 5%

#22 of 739 in Artificial Intelligence & Machine Learning

Academic recognition (35%)
66
Submission selectivity (20%)
83
Editions held (20%)
79
Community attention (10%)
79
Public record completeness (15%)
85

Inputs used: Listed as CCF C, ICORE C · Acceptance rate: 26.3% (mean of 5 editions on file) · Editions on record: 18 · Researchers following it here: 303 · Researchers who opened this page in the past 24 months: 25

Missing from the public record: Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

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 2026-09-18

Call For Papers

ACML 2026 (Asian Conference on Machine Learning) is a CCF C / ICORE C conference held in Melbourne, Australia on 2026-12-01. The paper submission deadline is 2026-07-05 (extended). Acceptance notifications are sent on 2026-09-15.

Topics Topics of interest include but are not limited to: General machine learning Active learning Bayesian machine learning Clustering Imitation Learning Learning to Rank Meta-Learning Multi-objective learning Multiple instance learning Multi-task learning Neuro-symbolic methods Online learning Optimization Reinforcement learning Relational learning Self-supervised learning Semi-supervised learning Structured output learning Supervised learning Transfer learning Unsupervised learning Weakly-supervised learning Learning with noisy labels Continual / lifelong learning Few-shot and zero-shot learning Out-of-distribution generalization Other machine learning methodologies Deep learning Architectures Deep reinforcement learning Generative models Multi-modality learning Large-language models and other foundation models Deep learning theory Representation learning Other topics in deep learning Generative AI Multimodal generative models Controllable and conditional generation Editing, inpainting, and style transfer Evaluation Creative applications (art, music, media) Theory Bandits Computational learning theory Game theory Optimization Statistical learning theory Other theories Datasets and reproducibility Implementations, libraries ML datasets and benchmarks Synthetic data generation Other topics in reproducible ML research Trustworthy machine learning Accountability, explainability, transparency Adversarial learning Causality Fairness Privacy Robustness AutoML AI safety and alignment Hallucination mitigation and reliability Other topics in trustworthy ML Learning in knowledge-intensive systems Knowledge refinement and theory revision Multi-strategy learning Retrieval-augmented generation (RAG) Knowledge-enhanced foundation models Other systems Applications Bioinformatics Biomedical informatics Climate science Collaborative filtering Computer vision Healthcare Human activity recognition Information retrieval Natural language processing Social good Social networks Web search ML for science discovery Other applications
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Acceptance Ratio

Average acceptance rate: 27.2% over 10 years (2012–2021).

YearSubmittedAcceptedAccepted(%)
202144113029.5%
20202116028.4%
20193438725.4%
20182656424.2%
20171954724.1%
20161473423.1%
2015962829.2%
2014802531.3%
20131033231.1%
20121383626.1%

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