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ORCIDPKDD / ECML 2023: Turin, Italy - Part I
- Danai Koutra
, Claudia Plant, Manuel Gomez-Rodriguez, Elena Baralis, Francesco Bonchi:
Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference, ECML PKDD 2023, Turin, Italy, September 18-22, 2023, Proceedings, Part I. Lecture Notes in Computer Science 14169, Springer 2023, ISBN 978-3-031-43411-2
Active Learning
- Tim Bakker, Herke van Hoof, Max Welling:
Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes. 3-19 - Cheng Chen, Yong Wang, Lizi Liao, Yueguo Chen, Xiaoyong Du:
Real: A Representative Error-Driven Approach for Active Learning. 20-37 - Gabriele Ciravegna, Frédéric Precioso, Alessandro Betti, Kevin Mottin, Marco Gori:
Knowledge-Driven Active Learning. 38-54 - Lukas Rauch, Matthias Aßenmacher, Denis Huseljic, Moritz Wirth, Bernd Bischl, Bernhard Sick:
ActiveGLAE: A Benchmark for Deep Active Learning with Transformers. 55-74 - Sandra Gilhuber, Julian Busch, Daniel Rotthues, Christian M. M. Frey, Thomas Seidl:
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification. 75-91
Adversarial Machine Learning
- Yuting Yang, Pei Huang, Juan Cao, Feifei Ma, Jian Zhang, Jintao Li:
Quantifying Robustness to Adversarial Word Substitutions. 95-112 - Tianjin Huang, Shiwei Liu, Tianlong Chen, Meng Fang, Li Shen, Vlado Menkovski, Lu Yin, Yulong Pei, Mykola Pechenizkiy:
Enhancing Adversarial Training via Reweighting Optimization Trajectory. 113-130 - Keizaburo Nishikino, Kenichi Kobayashi:
Adversarial Imitation Learning with Controllable Rewards for Text Generation. 131-146 - Zhiyu Zhu, Jiayu Zhang, Zhibo Jin, Xinyi Wang, Minhui Xue, Jun Shen, Kim-Kwang Raymond Choo, Huaming Chen:
Towards Minimising Perturbation Rate for Adversarial Machine Learning with Pruning. 147-163 - Skander Karkar, Patrick Gallinari, Alain Rakotomamonjy:
Adversarial Sample Detection Through Neural Network Transport Dynamics. 164-181
Anomaly Detection
- Jindong Li, Qianli Xing, Qi Wang, Yi Chang:
CVTGAD: Simplified Transformer with Cross-View Attention for Unsupervised Graph-Level Anomaly Detection. 185-200 - Chaoxi Niu, Guansong Pang, Ling Chen:
Graph-Level Anomaly Detection via Hierarchical Memory Networks. 201-218 - Timo Martens, Lorenzo Perini, Jesse Davis:
Semi-supervised Learning from Active Noisy Soft Labels for Anomaly Detection. 219-236 - Anastasiia Sedova, Lena Zellinger, Benjamin Roth:
Learning with Noisy Labels by Adaptive Gradient-Based Outlier Removal. 237-253 - Jaemin Yoo, Yue Zhao, Lingxiao Zhao, Leman Akoglu:
DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection. 254-269 - Mike Wong, Edward Raff, James Holt, Ravi Netravali:
Marvolo: Programmatic Data Augmentation for Deep Malware Detection. 270-285 - Jingrui Zhang, Ninh Pham, Gillian Dobbie:
A Transductive Forest for Anomaly Detection with Few Labels. 286-301
Applications
- Bowen Xing, Ivor W. Tsang:
Co-Evolving Graph Reasoning Network for Emotion-Cause Pair Extraction. 305-322 - Chen Li, Yoshihiro Yamanishi:
SpotGAN: A Reverse-Transformer GAN Generates Scaffold-Constrained Molecules with Property Optimization. 323-338 - Jia Hu, Chu Wang, Xianghong Lin:
Spatio-Temporal Pyramid Networks for Traffic Forecasting. 339-354 - Rishabh Upadhyay, Gabriella Pasi, Marco Viviani:
A Passage Retrieval Transformer-Based Re-Ranking Model for Truthful Consumer Health Search. 355-371 - Fei Liu, Yibo Li, Meiyun Zuo:
KESHEM: Knowledge Enabled Short Health Misinformation Detection Framework. 372-388 - Qin Lei, Jiang Zhong, Chen Wang, Yang Xia, Yangmei Zhou:
Dynamic Thresholding for Accurate Crack Segmentation Using Multi-objective Optimization. 389-404 - Zhangyang Gao, Cheng Tan, Jun Xia, Stan Z. Li:
Co-supervised Pre-training of Pocket and Ligand. 405-421 - Jiaming Li, Lang Lang, Zhenlong Zhu, Haozhao Wang, Ruixuan Li, Wenchao Xu:
Decompose, Then Reconstruct: A Framework of Network Structures for Click-Through Rate Prediction. 422-437 - Andrzej Dulny, Andreas Hotho, Anna Krause:
DynaBench: A Benchmark Dataset for Learning Dynamical Systems from Low-Resolution Data. 438-455
Bayesian Methods
- Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer:
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry. 459-474 - Ali Khoshvishkaie, Petrus Mikkola, Pierre-Alexandre Murena, Samuel Kaski:
Cooperative Bayesian Optimization for Imperfect Agents. 475-490 - Breeshey Roskams-Hieter, Jude Wells, Sara Wade:
Leveraging Variational Autoencoders for Multiple Data Imputation. 491-506 - Anis Fradi, Chafik Samir:
A New Framework for Classifying Probability Density Functions. 507-522
Causality
- Debo Cheng, Ziqi Xu, Jiuyong Li, Lin Liu, Thuc Duy Le, Jixue Liu:
Learning Conditional Instrumental Variable Representation for Causal Effect Estimation. 525-540 - Johannes Huegle, Christopher Hagedorn, Rainer Schlosser:
A KNN-Based Non-Parametric Conditional Independence Test for Mixed Data and Application in Causal Discovery. 541-558 - Paras Sheth, Tharindu Kumarage, Raha Moraffah, Aman Chadha, Huan Liu:
PEACE: Cross-Platform Hate Speech Detection - A Causality-Guided Framework. 559-575 - Xiaofeng Lin, Guoxi Zhang, Xiaotian Lu, Han Bao, Koh Takeuchi, Hisashi Kashima:
Estimating Treatment Effects Under Heterogeneous Interference. 576-592 - Krzysztof Rudas, Szymon Jaroszewicz:
Regularization for Uplift Regression. 593-608
Clustering
- Gaël Poux-Médard, Julien Velcin, Sabine Loudcher:
Powered Dirichlet Process - Controlling the "Rich-Get-Richer" Assumption in Bayesian Clustering. 611-626 - Michal Znalezniak, Przemyslaw Rola, Patryk Kaszuba, Jacek Tabor, Marek Smieja:
Contrastive Hierarchical Clustering. 627-643 - Yimei Zheng, Caiyan Jia, Jian Yu:
Contrastive Learning with Cluster-Preserving Augmentation for Attributed Graph Clustering. 644-661 - Mauritius Klein, Collin Leiber, Christian Böhm:
k-SubMix: Common Subspace Clustering on Mixed-Type Data. 662-677 - Mingyu Zhao, Weidong Yang, Feiping Nie:
Transformer-Based Contrastive Multi-view Clustering via Ensembles. 678-694 - Giulia Marchello, Marco Corneli, Charles Bouveyron:
A Deep Dynamic Latent Block Model for the Co-Clustering of Zero-Inflated Data Matrices. 695-710 - Corey J. Nolet, Divye Gala, Alexandre Fender, Mahesh Doijade, Joe Eaton, Edward Raff, John Zedlewski, Brad Rees, Tim Oates:
cuSLINK: Single-Linkage Agglomerative Clustering on the GPU. 711-726 - Sruthi Gorantla, Kishen N. Gowda, Amit Deshpande, Anand Louis:
Socially Fair Center-Based and Linear Subspace Clustering. 727-742 - Thibault Marette, Pauli Miettinen, Stefan Neumann:
Visualizing Overlapping Biclusterings and Boolean Matrix Factorizations. 743-758
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