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AusDM 2026: Australasian Data Science and Machine Learning Conference

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AusDM
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截稿日期:
2026-07-05
通知日期:
2026-08-10
会议日期:
2026-12-02
会议地点:
Sydney, Australia
届数:
ICORE: C   浏览: 20594   关注: 2   参加: 0

会伴指数 (CP-I)

58.7 / 100
全站第 643 名 / 共 5,684 个会议 · 前 12%

数据挖掘与数据库 第 34 / 337 人工智能与机器学习 第 54 / 741

学术认可 (35%)
58
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
87
社区关注 (10%)
29
资料公开度 (15%)
55

用到的输入: 收录等级：ICORE C · 有据可查的届次：24 · 在会伴关注它的研究者：2 人 · 过去 24 个月打开过本页的研究者：10 人

公开资料里还缺: 历年录用率 (+4.5) · 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 80% —— 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 2026-09-23

征稿

AusDM 2026 (Australasian Data Science and Machine Learning Conference) is a ICORE C conference held in Sydney, Australia on 2026-12-02. The paper submission deadline is 2026-07-05. Acceptance notifications are sent on 2026-08-10.

The Australasian Data Science and Machine Learning Conference (AusDM), formerly known as the Australasian Data Mining Conference, has become the leading regional forum for both researchers and practitioners in Data Science and Machine Learning, including areas such as Data Mining, Data Analytics, Deep Learning, Natural Language Processing, and Generative AI. As these interdisciplinary fields continue to evolve, AusDM remains committed to advancing the development and application of intelligent algorithms capable of learning from large-scale, complex data. The conference plays a vital role in sharing progress and fostering collaboration within the Australasian data science community, while also showcasing breakthroughs with global relevance. Since its inception in 2002, AusDM has provided a platform for the presentation, discussion, and dissemination of advances in algorithms, systems, software, and real-world applications. AusDM'26 will build on this tradition by encouraging cross-disciplinary exchange of ideas, practical experiences, and future research directions. AusDM'26 aims to be a dynamic meeting point for advancing the frontiers of Data Science and Machine Learning in both academia and industry. The program will feature keynote talks, panel sessions, paper presentations, workshops and tutorials, a doctoral consortium, and networking events, ensuring a rich and engaging experience for all participants. Topics of Interest We seek contributions in, but not limited to, the following areas: Foundational Techniques in Machine Learning and AI Supervised, unsupervised, semi-supervised and self-supervised learning. Deep learning and representation learning. Reinforcement learning and federated learning. Transfer learning, meta learning, few-shot and continual learning. Multitask and multimodal learning. Generative models, including GANs and diffusion models. Large Language Models (LLMs) and Large Multimodal Models (LMMs). Zero-shot and prompt-based learning. Learning from Diverse and Complex Data Analytics over structured, semi-structured, and unstructured data. Text, time-series, graph, spatial, spatio-temporal, and network data. Web, social media, multimedia, IoT, and sensor data. Sequential, temporal, and dynamic data modelling. Data-Centric AI and Data Engineering Data preprocessing, cleaning, integration, matching, and linkage. Privacy-preserving and secure data mining. Data-centric AI pipelines and dataset curation. Computational aspects of data mining and large-scale data management. Scalable and Real-Time Data Analytics Big data analytics and scalable ML. Parallel and distributed learning algorithms. Data stream mining and real-time analytics. Edge, cloud, and IoT-enabled ML systems. Interactive and Visual Analytics Visual analytics and explainability through visualisation. Human-in-the-loop machine learning. Interactive data exploration and decision support. Responsible, Causal, and Explainable AI Explainable and interpretable machine learning. Fairness, accountability, transparency, and ethics in AI. Causal inference and causal machine learning. Robustness, generalization, and uncertainty quantification. Applied Data Science and ML Across Domains Applications in business, finance, education, agriculture, urban planning, healthcare, sports, social sciences, cybersecurity, arts, and humanities. Domain-specific AI systems in biomedical informatics, environmental science, astronomy, engineering, and more. Industrial case studies and data-driven product innovations.
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