会议信息

ACML 2026: Asian Conference on Machine Learning

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截稿日期:
2026-07-05 Extended
通知日期:
2026-09-15
会议日期:
2026-12-01
会议地点:
Melbourne, Australia
届数:
CCF: C   ICORE: C   浏览: 168005   关注: 303   参加: 64

会伴指数 (CP-I)

76.2 / 100
全站第 243 名 / 共 5,682 个会议 · 前 5%

人工智能与机器学习 第 22 / 739

学术认可 (35%)
66
投稿选择性 (20%)
83
会议传承 (20%)
79
社区关注 (10%)
79
资料公开度 (15%)
85

用到的输入: 收录等级：CCF C, ICORE C · 录用率：26.3%（有记录的 5 届的均值） · 有据可查的届次：18 · 在会伴关注它的研究者：303 人 · 过去 24 个月打开过本页的研究者：25 人

公开资料里还缺: 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 100% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-09-18

征稿

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
由 Admin Agent 最后更新于

录用率

平均录用率: 27.2% 10 年间 (2012–2021).

年份提交数录用数录用率(%)
202144113029.5%
20202116028.4%
20193438725.4%
20182656424.2%
20171954724.1%
20161473423.1%
2015962829.2%
2014802531.3%
20131033231.1%
20121383626.1%

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CCF全称影响因子出版商ISSN
BMachine Learning2.9Springer0885-6125
CKnowledge-Based Systems7.2Elsevier0950-7051
CFuture Generation Computer Systems5.9Elsevier0167-739X
CNeurocomputing6.5Elsevier0925-2312
CPattern Recognition Letters3.9Elsevier0167-8655
CIEEE Transactions on Industrial Informatics11.7IEEE1551-3203
CIEEE Internet of Things Journal8.9IEEE2327-4662
CEngineering Applications of Artificial Intelligence9.0Elsevier0952-1976
CExpert Systems with Applications7.5Elsevier0957-4174
CIEEE Transactions on Big Data5.7IEEE2332-7790

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