Advances in Knowledge Discovery and Data Mining: 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Pakdd 2025, Sydney, Nsw, Austral
暫譯: 知識發現與資料探勘的進展:第29屆亞太知識發現與資料探勘會議,Pakdd 2025,悉尼,澳洲

Wu, Xintao, Spiliopoulou, Myra, Wang, Can

  • 出版商: Springer
  • 出版日期: 2025-06-20
  • 售價: $3,760
  • 貴賓價: 9.5$3,572
  • 語言: 英文
  • 頁數: 454
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9819681820
  • ISBN-13: 9789819681822
  • 相關分類: Data-miningMachine LearningLarge language model
  • 海外代購書籍(需單獨結帳)

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商品描述

The five-volume set, LNAI 158710 - 15874 constitutes the proceedings of the 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, held in Sydney, New South Wales, Australia, during June 10-13, 2025.

The conference received a total of 557 submissions to the main track, 35 submissions to the survey track and 104 submittion to the special track on LLMs. Of these, 134 papers have been accepted for the main track, 10 for the survey track and 24 for the LLM track. 68 papers have been transferred to the4 DSFA special session.

The papers have been organized in topical sections as follows:

Part I: Anomaly Detection; Business Data Analysis; Clustering; Continual Learning; Contrastive Learning; Data Processing for Learning;

Part II: Fairness and Interpretability; Federated Learning; Graph Mining and GNN; Learning on Scientific Data;

Part III: Machine Learning; Multi-modality; OOD and Optimization; Recommender Systems; Representation Learning and Generative AI;

Part IV: Security and Privacy; Temporal Learning; Survey;

Part V: LLM Fine-tuning and Prompt Engineering; Fairness and Interpretability of LLMs; LLM Application; OOD and Optimization of LLMs.

商品描述(中文翻譯)

五卷本的 LNAI 158710 - 15874 是第29屆亞太知識發現與數據挖掘會議(PAKDD 2025)的會議論文集,該會議於2025年6月10日至13日在澳大利亞新南威爾士州的悉尼舉行。

會議共收到557篇主題投稿、35篇調查投稿以及104篇針對大型語言模型(LLMs)的特別投稿。其中,134篇論文被接受為主題投稿,10篇被接受為調查投稿,24篇被接受為LLM專題投稿。68篇論文已轉至DSFA特別會議。

論文已按主題分為以下幾個部分:

第一部分:異常檢測;商業數據分析;聚類;持續學習;對比學習;學習數據處理;

第二部分:公平性與可解釋性;聯邦學習;圖挖掘與圖神經網絡(GNN);科學數據上的學習;

第三部分:機器學習;多模態;異常檢測(OOD)與優化;推薦系統;表示學習與生成式人工智慧(Generative AI);

第四部分:安全性與隱私;時間序列學習;調查;

第五部分:LLM微調與提示工程;LLM的公平性與可解釋性;LLM應用;LLM的異常檢測(OOD)與優化。

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