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ORCIDPKDD / ECML 2022: Grenoble, France - Part I
- Nuria Oliver
, Fernando Pérez-Cruz, Stefan Kramer, Jesse Read, José Antonio Lozano:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings, Part I. Lecture Notes in Computer Science 12975, Springer 2021, ISBN 978-3-030-86485-9
Clustering and Dimensionality Reduction
- Xu Feng, Wenjian Yu, Yuyang Xie:
Pass-Efficient Randomized SVD with Boosted Accuracy. 3-20 - Hussein El Amouri, Thomas Andrew Lampert, Pierre Gançarski, Clément Mallet:
CDPS: Constrained DTW-Preserving Shapelets. 21-37 - Christopher M. A. Bonenberger, Wolfgang Ertel, Markus Schneider, Friedhelm Schwenker:
Structured Nonlinear Discriminant Analysis. 38-54 - Juncheng Liu, Yiwei Wang, Bryan Hooi, Renchi Yang, Xiaokui Xiao:
LSCALE: Latent Space Clustering-Based Active Learning for Node Classification. 55-70 - Jarne Verhaeghe, M. Jeroen Van Der Donckt, Femke Ongenae, Sofie Van Hoecke:
Powershap: A Power-Full Shapley Feature Selection Method. 71-87 - Zheng Chen, Lingwei Zhu, Ziwei Yang, Takashi Matsubara:
Automated Cancer Subtyping via Vector Quantization Mutual Information Maximization. 88-103 - Fynn Bachmann, Philipp Hennig, Dmitry Kobak:
Wasserstein t-SNE. 104-120 - Haruya Ishizuka, Daichi Mochihashi:
Nonparametric Bayesian Deep Visualization. 121-137 - Geping Yang, Hongzhang Lv, Yiyang Yang, Zhiguo Gong, Xiang Chen, Zhifeng Hao:
FastDEC: Clustering by Fast Dominance Estimation. 138-156 - Azqa Nadeem, Sicco Verwer:
SECLEDS: Sequence Clustering in Evolving Data Streams via Multiple Medoids and Medoid Voting. 157-173 - Nguyen-Viet-Dung Nghiem, Christel Vrain, Thi-Bich-Hanh Dao:
Knowledge Integration in Deep Clustering. 174-190
Anomaly Detection
- Adam Goodge, Bryan Hooi, See-Kiong Ng, Wee Siong Ng:
ARES: Locally Adaptive Reconstruction-Based Anomaly Scoring. 193-208 - Jan-Philipp Schulze, Philip Sperl, Ana Radutoiu, Carla Sagebiel, Konstantin Böttinger:
R2-AD2: Detecting Anomalies by Analysing the Raw Gradient. 209-224 - Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy:
Hop-Count Based Self-supervised Anomaly Detection on Attributed Networks. 225-241 - Bhumika, Debasis Das:
Deep Learning Based Urban Anomaly Prediction from Spatiotemporal Data. 242-257 - Lucas Cazzonelli, Cedric Kulbach:
Detecting Anomalies with Autoencoders on Data Streams. 258-274 - Shin Ando, Ayaka Yamamoto:
Anomaly Detection via Few-Shot Learning on Normality. 275-290
Interpretability and Explainability
- Yuki Oba, Taro Tezuka, Masaru Sanuki, Yukiko Wagatsuma:
Interpretations of Predictive Models for Lifestyle-related Diseases at Multiple Time Intervals. 293-308 - Charles Condevaux, Sébastien Harispe, Stéphane Mussard:
Fair and Efficient Alternatives to Shapley-based Attribution Methods. 309-324 - Gianluigi Lopardo, Damien Garreau, Frédéric Precioso, Greger Ottosson:
SMACE: A New Method for the Interpretability of Composite Decision Systems. 325-339 - Gregory Scafarto, Nicolas Posocco, Antoine Bonnefoy:
Calibrate to Interpret. 340-355 - Riccardo Massidda, Davide Bacciu:
Knowledge-Driven Interpretation of Convolutional Neural Networks. 356-371 - Housam Khalifa Bashier Babiker, Mi-Young Kim, Randy Goebel:
Neural Networks with Feature Attribution and Contrastive Explanations. 372-388 - Amr Alkhatib, Henrik Boström, Michalis Vazirgiannis:
Explaining Predictions by Characteristic Rules. 389-403 - Panagiotis Symeonidis, Lidija Kirjackaja, Markus Zanker:
Session-Based Recommendation Along with the Session Style of Explanation. 404-420 - Dawid Rymarczyk, Adam Pardyl, Jaroslaw Kraus, Aneta Kaczynska, Marek Skomorowski, Bartosz Zielinski:
ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification. 421-436 - Victor Guyomard, Françoise Fessant, Thomas Guyet, Tassadit Bouadi, Alexandre Termier:
VCNet: A Self-explaining Model for Realistic Counterfactual Generation. 437-453
Ranking and Recommender Systems
- Carola Gajek, Alexander Schiendorfer, Wolfgang Reif:
A Recommendation System for CAD Assembly Modeling Based on Graph Neural Networks. 457-473 - Yifan Wang, Yifang Qin, Yu Han, Mingyang Yin, Jingren Zhou, Hongxia Yang, Ming Zhang:
AD-AUG: Adversarial Data Augmentation for Counterfactual Recommendation. 474-490 - Yabo Chu, Enneng Yang, Qiang Liu, Yuting Liu, Linying Jiang, Guibing Guo:
Bi-directional Contrastive Distillation for Multi-behavior Recommendation. 491-507 - Yisong Yu, Beihong Jin, Jiageng Song, Beibei Li, Yiyuan Zheng, Wei Zhuo:
Improving Micro-video Recommendation by Controlling Position Bias. 508-523 - Ming He, Xinlei Hu, Changshu Li, Xin Chen, Jiwen Wang:
Mitigating Confounding Bias for Recommendation via Counterfactual Inference. 524-540 - Srinivas Virinchi, Anoop Saladi, Abhirup Mondal:
Recommending Related Products Using Graph Neural Networks in Directed Graphs. 541-557 - Peng Yi, Xiongcai Cai, Ziteng Li:
A U-Shaped Hierarchical Recommender by Multi-resolution Collaborative Signal Modeling. 558-573 - Ting-Ting Su, Zhenyu He, Man-Sheng Chen, Chang-Dong Wang:
Basket Booster for Prototype-based Contrastive Learning in Next Basket Recommendation. 574-589 - Mengyuan Jing, Yanmin Zhu, Tianzi Zang, Jiadi Yu, Feilong Tang:
Graph Contrastive Learning with Adaptive Augmentation for Recommendation. 590-605 - Shicheng Wang, Shu Guo, Lihong Wang, Tingwen Liu, Hongbo Xu:
Multi-interest Extraction Joint with Contrastive Learning for News Recommendation. 606-621
Transfer and Multitask Learning
- Lukasz Maziarka, Aleksandra Nowak, Maciej Wolczyk, Andrzej Bedychaj:
On the Relationship Between Disentanglement and Multi-task Learning. 625-641 - Yuntao Du, Hongtao Luo, Haiyang Yang, Juan Jiang, Chongjun Wang:
InCo: Intermediate Prototype Contrast for Unsupervised Domain Adaptation. 642-658 - Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis:
Fast and Accurate Importance Weighting for Correcting Sample Bias. 659-674 - Yunfei Teng, Anna Choromanska, Murray Campbell, Songtao Lu, Parikshit Ram, Lior Horesh:
Overcoming Catastrophic Forgetting via Direction-Constrained Optimization. 675-692 - Shibal Ibrahim, Natalia Ponomareva, Rahul Mazumder:
Newer is Not Always Better: Rethinking Transferability Metrics, Their Peculiarities, Stability and Performance. 693-709 - Arjun Roy, Eirini Ntoutsi:
Learning to Teach Fairness-Aware Deep Multi-task Learning. 710-726
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