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Advances in knowledge discovery and data mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings. Part II / De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin, editors.

By: Contributor(s): Material type: TextTextSeries: Lecture notes in computer science. Lecture notes in artificial intelligence. | Lecture notes in computer science ; 14646. | LNCS sublibrary. SL 7, Artificial intelligence.Publisher: Singapore : Springer, 2024Description: 1 online resource (xxxiv, 459 pages) : illustrations (some color)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9789819722532
  • 9819722535
Other title:
  • PAKDD 2024
Subject(s): Additional physical formats: No titleDDC classification:
  • 006.3/12 23/eng/20240501
LOC classification:
  • QA76.9.D343
Online resources:
Contents:
Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part II -- Deep Learning -- AdaPQ: Adaptive Exploration Product Quantization with Adversary-Aware Block Size Selection Toward Compression Efficiency -- 1 Introduction -- 2 Related Works -- 3 Preliminary -- 4 Methodology -- 4.1 Adaptive Exploration Quantization -- 4.2 Adversary-Aware Block Size Selection -- 5 Experiments -- 6 Conclusion -- References -- Ranking Enhanced Supervised Contrastive Learning for Regression -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Methodology -- 4.1 Motivation
4.2 Ranking Enhanced Supervised Contrastive Learning (RESupCon) -- 5 Experiments -- 5.1 Datasets -- 5.2 Baselines and Settings -- 5.3 Overall Performance -- 5.4 Comparison on Spearman's Rank Correlation Coefficients -- 5.5 Parameter Study and Loss Curve -- 6 Conclusion -- References -- Treatment Effect Estimation Under Unknown Interference -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Proposed Method: Treatment Effect Estimation Under Unknown Interference -- 4.1 Covariate Representation Learner -- 4.2 Graph Structure Learner -- 4.3 Aggregation Function
4.4 Outcome Predictors and ITE Estimators -- 5 Experiments -- 5.1 Experiment Settings -- 5.2 Results -- 6 Conclusion -- A Identifiability of the Expectation of Potential Outcomes -- B HSIC -- C Implementation Details -- D Ablation Experiments -- References -- A New Loss for Image Retrieval: Class Anchor Margin -- 1 Introduction -- 2 Related Work -- 3 Method -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Setup -- 4.3 Results -- 5 Conclusion -- References -- Personalized EDM Subject Generation via Co-factored User-Subject Embedding -- 1 Introduction -- 2 Related Work -- 3 Proposed Model
3.1 Retrieve and Re-rank -- 3.2 Variational Encoder and Bi-directional Selective Encoder -- 3.3 User-Subject Co-factor System -- 3.4 User-Based Decoder -- 4 Experimental Results -- 4.1 Quantitative Results -- 4.2 Effect of Template -- 5 Conclusions and Future Work -- References -- Spatial-Temporal Bipartite Graph Attention Network for Traffic Forecasting -- 1 Introduction -- 2 Related Work -- 3 Definitions and Problem Statement -- 3.1 Definitions -- 3.2 Problem Statement -- 4 Methodology -- 4.1 Data Inputs and Data Preprocessing -- 4.2 Encoder Decoder Architecture
4.3 Bipartite Graph Attention Layer -- 4.4 Heterogeneous Cross Attention Layers -- 5 Experiments -- 5.1 Experiment Setup -- 5.2 Comparison of Performance -- 5.3 Ablation Study -- 6 Conclusion and Future Works -- References -- CMed-GPT: Prompt Tuning for Entity-Aware Chinese Medical Dialogue Generation -- 1 Introduction -- 2 Related Work -- 3 Datasets -- 4 Method -- 4.1 Pre-training Model -- 4.2 Medical Dialogue Generation Model -- 5 Experiments -- 5.1 Experimental Setting -- 5.2 Experimental Results -- 6 Conclusion -- References
In: Springer Nature eBookSummary: The 6-volume set LNAI 14645-14650 constitutes the proceedings of the 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, which took place in Taipei, Taiwan, during May 7-10, 2024. The 177 papers presented in these proceedings were carefully reviewed and selected from 720 submissions. They deal with new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, big data technologies, and foundations.
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The 6-volume set LNAI 14645-14650 constitutes the proceedings of the 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, which took place in Taipei, Taiwan, during May 7-10, 2024. The 177 papers presented in these proceedings were carefully reviewed and selected from 720 submissions. They deal with new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, big data technologies, and foundations.

Includes author index.

Online resource; title from PDF title page (SpringerLink, viewed May 1, 2024).

Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part II -- Deep Learning -- AdaPQ: Adaptive Exploration Product Quantization with Adversary-Aware Block Size Selection Toward Compression Efficiency -- 1 Introduction -- 2 Related Works -- 3 Preliminary -- 4 Methodology -- 4.1 Adaptive Exploration Quantization -- 4.2 Adversary-Aware Block Size Selection -- 5 Experiments -- 6 Conclusion -- References -- Ranking Enhanced Supervised Contrastive Learning for Regression -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Methodology -- 4.1 Motivation

4.2 Ranking Enhanced Supervised Contrastive Learning (RESupCon) -- 5 Experiments -- 5.1 Datasets -- 5.2 Baselines and Settings -- 5.3 Overall Performance -- 5.4 Comparison on Spearman's Rank Correlation Coefficients -- 5.5 Parameter Study and Loss Curve -- 6 Conclusion -- References -- Treatment Effect Estimation Under Unknown Interference -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Proposed Method: Treatment Effect Estimation Under Unknown Interference -- 4.1 Covariate Representation Learner -- 4.2 Graph Structure Learner -- 4.3 Aggregation Function

4.4 Outcome Predictors and ITE Estimators -- 5 Experiments -- 5.1 Experiment Settings -- 5.2 Results -- 6 Conclusion -- A Identifiability of the Expectation of Potential Outcomes -- B HSIC -- C Implementation Details -- D Ablation Experiments -- References -- A New Loss for Image Retrieval: Class Anchor Margin -- 1 Introduction -- 2 Related Work -- 3 Method -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Setup -- 4.3 Results -- 5 Conclusion -- References -- Personalized EDM Subject Generation via Co-factored User-Subject Embedding -- 1 Introduction -- 2 Related Work -- 3 Proposed Model

3.1 Retrieve and Re-rank -- 3.2 Variational Encoder and Bi-directional Selective Encoder -- 3.3 User-Subject Co-factor System -- 3.4 User-Based Decoder -- 4 Experimental Results -- 4.1 Quantitative Results -- 4.2 Effect of Template -- 5 Conclusions and Future Work -- References -- Spatial-Temporal Bipartite Graph Attention Network for Traffic Forecasting -- 1 Introduction -- 2 Related Work -- 3 Definitions and Problem Statement -- 3.1 Definitions -- 3.2 Problem Statement -- 4 Methodology -- 4.1 Data Inputs and Data Preprocessing -- 4.2 Encoder Decoder Architecture

4.3 Bipartite Graph Attention Layer -- 4.4 Heterogeneous Cross Attention Layers -- 5 Experiments -- 5.1 Experiment Setup -- 5.2 Comparison of Performance -- 5.3 Ablation Study -- 6 Conclusion and Future Works -- References -- CMed-GPT: Prompt Tuning for Entity-Aware Chinese Medical Dialogue Generation -- 1 Introduction -- 2 Related Work -- 3 Datasets -- 4 Method -- 4.1 Pre-training Model -- 4.2 Medical Dialogue Generation Model -- 5 Experiments -- 5.1 Experimental Setting -- 5.2 Experimental Results -- 6 Conclusion -- References

Includes bibliographical references and index.

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