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AusDM 2026: Australasian Data Science and Machine Learning Conference

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AusDM
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投稿締切日:
2026-07-05
通知日:
2026-08-10
開催日:
2026-12-02
開催地:
Sydney, Australia
開催回数:
ICORE: C   閲覧: 20593   フォロー: 2   参加: 0

会伴インデックス (CP-I)

58.7 / 100
全 5,684 件中 第 643 位 · 上位 12%

データマイニング・データベース 分野 337 件中 第 34 位 人工知能・機械学習 分野 741 件中 第 54 位

学術的評価 (35%)
58
投稿の選択性 (20%) データなし — 中立の基準値 50 点として算入 —
開催回数 (20%)
87
コミュニティの注目度 (10%)
29
公開情報の充実度 (15%)
55

使用した入力: 収録ランク：ICORE C · 確認できる開催回数：24 · 会伴でフォローしている研究者：2 人 · 過去 24 か月にこのページを開いた研究者：10 人

公開情報で不足しているもの: 過去の採択率 (+4.5) · 最優秀論文の記録 (+2.3)
主催者は会議を認証申請したうえで、このページから直接追加できます。スコアは毎晩再計算されます。このスコアを上げるには

信頼度 80% — スコアのうち、中立の基準値ではなく実際に観測されたデータに基づく割合。 このスコアの算出方法 · ランキングを見る · アルゴリズム版 1.1 · 算出日 2026-09-23

論文募集

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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