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ORCID27th PAKDD 2023: Osaka, Japan - Part II
- Hisashi Kashima
, Tsuyoshi Idé, Wen-Chih Peng:
Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25-28, 2023, Proceedings, Part II. Lecture Notes in Computer Science 13936, Springer 2023, ISBN 978-3-031-33376-7
Graphs and Networks
- Feng Xie, Xiang Zeng, Bin Zhou, Yusong Tan:
Improving Knowledge Graph Entity Alignment with Graph Augmentation. 3-14 - Thanh Le, An Pham, Tho Chung, Truong Nguyen, Tuan Nguyen, Bac Le:
MixER: MLP-Mixer Knowledge Graph Embedding for Capturing Rich Entity-Relation Interactions in Link Prediction. 15-27 - Siyue Xie, Yiming Li, Da Sun Handason Tam, Xiaxin Liu, Qiufang Ying, Wing Cheong Lau, Dah Ming Chiu, Shou Zhi Chen:
GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge Aggregation. 28-39 - Huaisheng Zhu, Xianfeng Tang, Tianxiang Zhao, Suhang Wang:
You Need to Look Globally: Discovering Representative Topology Structures to Enhance Graph Neural Network. 40-52 - Yen-Ching Tseng, Zu-Mu Chen, Mi-Yen Yeh, Shou-De Lin:
UPGAT: Uncertainty-Aware Pseudo-neighbor Augmented Knowledge Graph Attention Network. 53-65 - Zhi Cheng, Landy Andriamampianina, Franck Ravat, Jiefu Song, Nathalie Vallès-Parlangeau, Philippe Fournier-Viger, Nazha Selmaoui-Folcher:
Mining Frequent Sequential Subgraph Evolutions in Dynamic Attributed Graphs. 66-78 - Nils Henke, Shimon Wonsak, Prasenjit Mitra, Michael Nolting, Nicolas Tempelmeier:
CondTraj-GAN: Conditional Sequential GAN for Generating Synthetic Vehicle Trajectories. 79-91 - Yifu Guo, Yong Liu:
A Graph Contrastive Learning Framework with Adaptive Augmentation and Encoding for Unaligned Views. 92-104 - Muhammad Ifte Khairul Islam, Max Khanov, Esra Akbas:
MPool: Motif-Based Graph Pooling. 105-117 - Woochang Hyun, Jaehong Lee, Bongwon Suh:
Anti-Money Laundering in Cryptocurrency via Multi-Relational Graph Neural Network. 118-130
Interpretability and Explainability
- Tri Dung Duong, Qian Li, Guandong Xu:
CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data Using Normalizing Flows. 133-144 - Zidi Xiu, Kai-Chen Cheng, David Q. Sun, Jiannan Lu, Hadas Kotek, Yuhan Zhang, Paul McCarthy, Christopher Klein, Stephen Pulman, Jason D. Williams:
Feedback Effect in User Interaction with Intelligent Assistants: Delayed Engagement, Adaption and Drop-out. 145-158 - Jisoo Jang, Mina Kim, Tien-Cuong Bui, Wen-Syan Li:
Toward Interpretable Machine Learning: Constructing Polynomial Models Based on Feature Interaction Trees. 159-170
Kernel Methods
- Sarwan Ali, Usama Sardar, Murray Patterson, Imdadullah Khan:
BioSequence2Vec: Efficient Embedding Generation for Biological Sequences. 173-185
Matrices and Tensors
- Hansi Jiang, Carl Meyer:
Relations Between Adjacency and Modularity Graph Partitioning. 189-200
Model Selection and Evaluation
- Zhihao Liu, Weiming Ou, Songhao Wang:
Bayesian Optimization over Mixed Type Inputs with Encoding Methods. 203-215
Online and Streaming Algorithms
- Wernsen Wong, Yun Sing Koh, Gillian Dobbie:
Using Flexible Memories to Reduce Catastrophic Forgetting. 219-230 - Aadityan Ganesh, Pratik Ghosal, Vishwa Prakash HV, Prajakta Nimbhorkar:
Fair Healthcare Rationing to Maximize Dynamic Utilities. 231-242 - Jinyu Mo, Hong Xie:
A Multi-player MAB Approach for Distributed Selection Problems. 243-254 - Hanxuan Xu, Hong Xie:
A Thompson Sampling Approach to Unifying Causal Inference and Bandit Learning. 255-266
Parallel and Distributed Mining
- Jiyue Huang, Chi Hong, Yang Liu, Lydia Y. Chen, Stefanie Roos:
Maverick Matters: Client Contribution and Selection in Federated Learning. 269-282 - Yongli Mou, Jiahui Geng, Feng Zhou, Oya Beyan, Chunming Rong, Stefan Decker:
pFedV: Mitigating Feature Distribution Skewness via Personalized Federated Learning with Variational Distribution Constraints. 283-294
Probabilistic Models and Statistical Inference
- Masahiro Kohjima, Takeshi Kurashima, Hiroyuki Toda:
Inverse Problem of Censored Markov Chain: Estimating Markov Chain Parameters from Censored Transition Data. 297-308 - Mina Rafla, Nicolas Voisine, Bruno Crémilleux:
Parameter-Free Bayesian Decision Trees for Uplift Modeling. 309-321 - Yixiao Lu, Yokiu Lee, Haoran Feng, Johnathan Leung, Alvin Cheung, Katharina Dost, Katerina Taskova, Thomas Lacombe:
Interpretability Meets Generalizability: A Hybrid Machine Learning System to Identify Nonlinear Granger Causality in Global Stock Indices. 322-334
Reinforcement Learning
- Sepideh Nahali, Hajer Ayadi, Jimmy X. Huang, Esmat Pakizeh, Mir Mohsen Pedram, Leila Safari:
A Dynamic and Task-Independent Reward Shaping Approach for Discrete Partially Observable Markov Decision Processes. 337-348 - Xin Du, Jiahai Wang, Siyuan Chen:
Multi-Agent Meta-Reinforcement Learning with Coordination and Reward Shaping for Traffic Signal Control. 349-360 - Talal Algumaei, Ruben Solozabal, Réda Alami, Hakim Hacid, Mérouane Debbah, Martin Takác:
Regularization of the Policy Updates for Stabilizing Mean Field Games. 361-372 - David Winkel, Niklas Strauß, Matthias Schubert, Yunpu Ma, Thomas Seidl:
Constrained Portfolio Management Using Action Space Decomposition for Reinforcement Learning. 373-385 - Siqi Chen, Tianpei Yang, Heng You, Jianing Zhao, Jianye Hao, Gerhard Weiss:
Transfer Reinforcement Learning Based Negotiating Agent Framework. 386-397
Relational Learning
- Xiaoge Li, Dayuan Guo, Tiantian Wang:
A Relational Instance-Based Clustering Method with Contrastive Learning for Open Relation Extraction. 401-411
Security and Privacy
- Xingxing Tang, Hanlin Gu, Lixin Fan, Qiang Yang:
Achieving Provable Byzantine Fault-tolerance in a Semi-honest Federated Learning Setting. 415-427 - Najeeb Moharram Jebreel, Josep Domingo-Ferrer, Yiming Li:
Defending Against Backdoor Attacks by Layer-wise Feature Analysis. 428-440 - Mark Huasong Meng, Sin G. Teo, Guangdong Bai, Kailong Wang, Jin Song Dong:
Enhancing Federated Learning Robustness Using Data-Agnostic Model Pruning. 441-453 - Hai Zhu, Qinyang Zhao, Yuren Wu:
BeamAttack: Generating High-quality Textual Adversarial Examples Through Beam Search and Mixed Semantic Spaces. 454-465
Semi-supervised and Unsupervised Learning
- Wei-I Lin, Hsuan-Tien Lin:
Reduction from Complementary-Label Learning to Probability Estimates. 469-481 - Yu Xia, Kai Zhang, Kaijie Zhou, Rui Wang, Xiaohui Hu:
Semi-Supervised Text Classification via Self-Paced Semantic-Level Contrast. 482-494 - Xu Li, Yongsheng Chen:
Multi-Augmentation Contrastive Learning as Multi-Objective Optimization for Graph Neural Networks. 495-507 - Xiaolin Pang, Kexin Xie, Yuxi Zhang, Max Fleming, Damian Chen Xu, Wei Liu:
Adversarial Active Learning with Guided BERT Feature Encoding. 508-520
Theoretical Foundations
- Fan Sha, Jianyu Pan:
Accelerating Stochastic Newton Method via Chebyshev Polynomial Approximation. 523-534 - Gözde Özcan, Stratis Ioannidis:
Stochastic Submodular Maximization via Polynomial Estimators. 535-548
Transfer Learning and Meta Learning
- Rafael Rêgo Drumond, Lukas Brinkmeyer, Lars Schmidt-Thieme:
Few-Shot Human Motion Prediction for Heterogeneous Sensors. 551-563
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