会议信息

ARRL 2024: International Workshop on Adaptable, Reliable, and Responsible Learning

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截稿日期:
2024-09-10
通知日期:
2024-10-07
会议日期:
2024-12-09
会议地点:
Abu Dhabi, UAE
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证据有限：这个会议不在 CCF / ICORE / QUALIS 任何一份榜单里，也没有录用率数据，因此分数的大部分回落到了中性基准。
学术认可 (35%) 无数据 —— 按中性基准 50 分计入 —
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
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社区关注 (10%)
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35

用到的输入: 过去 24 个月打开过本页的研究者：3 人

公开资料里还缺: 历年录用率 (+4.5) · 历届信息 (+3.0) · 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 25% —— 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 2026-10-07

征稿

ARRL 2024 (International Workshop on Adaptable, Reliable, and Responsible Learning) is an academic conference held in Abu Dhabi, UAE on 2024-12-09. The paper submission deadline is 2024-09-10. Acceptance notifications are sent on 2024-10-07.

Theory, methodology, and resource papers are welcome from any of the following areas, including but not limited to: Adaptable Learning Online/Incremental Learning Transfer Learning and Domain Adaptation Lifelong/Continual/Meta Learning Learning from Heterogeneous and Multi-Modal Data Knowledge Discovery from Multiple Databases Learning with Rejection/Abstention Cross-Domain Data Mining Evolving Data Stream Mining Ensemble Learning in Dynamic Environments Reliable Learning Robustness and Generalization in Data Mining Trustworthiness in Learning-enabled Systems Noise Handling and Outlier/Anomaly Detection Data Wrangling and Munging for Reliable Preprocessing Data Quality Assessment and Assurance Robustness in Graph and Network Mining Uncertainty Quantification and Confidence Estimation in Learning-enabled Systems Learning with Very Few Examples Open-World Learning (Learning in Unexpected/Unknown Environments) Responsible Learning Explainable Learning Modules and Architectures Interpretability of Learning Results Algorithmic Fairness in Data Mining Discrimination-aware Data Mining Privacy-Preserving Data Mining Ethical Data Mining and Data Usage Socio-technical Aspects of Data Mining Bias Detection and Mitigation in Learning-enabled Systems AI for Environmental and Social Sustainability Data Mining for Energy Efficiency and Climate Action
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