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ORCIDPKDD / ECML 2020: Ghent, Belgium
- Frank Hutter
, Kristian Kersting, Jefrey Lijffijt, Isabel Valera:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings, Part III. Lecture Notes in Computer Science 12459, Springer 2021, ISBN 978-3-030-67663-6
Combinatorial Optimization
- Hau Chan, Grigorios Loukides, Zhenghui Su:
Algorithms for Optimizing the Ratio of Monotone k-Submodular Functions. 3-19 - Polina Rozenshtein, Giulia Preti, Aristides Gionis, Yannis Velegrakis:
Mining Dense Subgraphs with Similar Edges. 20-36 - Zilong Bai, S. S. Ravi, Ian Davidson:
Towards Description of Block Model on Graph. 37-53
Large-Scale Optimization and Differential Privacy
- Tianyi Chen, Tianyu Ding, Bo Ji, Guanyi Wang, Yixin Shi, Jing Tian, Sheng Yi, Xiao Tu, Zhihui Zhu:
Orthant Based Proximal Stochastic Gradient Method for ℓ 1-Regularized Optimization. 57-73 - Qi Deng, Chenghao Lan:
Efficiency of Coordinate Descent Methods for Structured Nonconvex Optimization. 74-89 - Di Wang, Jinhui Xu:
Escaping Saddle Points of Empirical Risk Privately and Scalably via DP-Trust Region Method. 90-106
Boosting and Ensemble Methods
- Andrew M. Webb, Charles Reynolds, Wenlin Chen, Henry W. J. Reeve, Dan-Andrei Iliescu, Mikel Luján, Gavin Brown:
To Ensemble or Not Ensemble: When Does End-to-End Training Fail? 109-123 - Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz, Vu-Linh Nguyen, Eyke Hüllermeier:
Learning Gradient Boosted Multi-label Classification Rules. 124-140 - Léo Gautheron, Pascal Germain, Amaury Habrard, Guillaume Metzler, Emilie Morvant, Marc Sebban, Valentina Zantedeschi:
Landmark-Based Ensemble Learning with Random Fourier Features and Gradient Boosting. 141-157 - Andreas Bender, David Rügamer, Fabian Scheipl, Bernd Bischl:
A General Machine Learning Framework for Survival Analysis. 158-173 - James M. Hickey, Pietro G. Di Stefano, Vlasios Vasileiou:
Fairness by Explicability and Adversarial SHAP Learning. 174-190 - Takuya Konishi, Takuro Fukunaga:
End-to-End Learning for Prediction and Optimization with Gradient Boosting. 191-207
Bayesian Methods
- Giorgio Corani, Dario Azzimonti, João P. S. C. Augusto, Marco Zaffalon:
Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule. 211-226 - Lorenzo Perini, Vincent Vercruyssen, Jesse Davis:
Quantifying the Confidence of Anomaly Detectors in Their Example-Wise Predictions. 227-243
Architecture of Neural Networks
- Martin Wistuba:
XferNAS: Transfer Neural Architecture Search. 247-262 - Bartosz Wójcik, Maciej Wolczyk, Klaudia Balazy, Jacek Tabor:
Finding the Optimal Network Depth in Classification Tasks. 263-278 - Shiwei Liu, Tim van der Lee, Anil Yaman, Zahra Atashgahi, Davide Ferraro, Ghada Sokar, Mykola Pechenizkiy, Decebal Constantin Mocanu:
Topological Insights into Sparse Neural Networks. 279-294
Graph Neural Networks
- M. Vijaikumar, Shirish K. Shevade, M. Narasimha Murty:
GRAM-SMOT: Top-N Personalized Bundle Recommendation via Graph Attention Mechanism and Submodular Optimization. 297-313 - Yugang Ji, Mingyang Yin, Yuan Fang, Hongxia Yang, Xiangwei Wang, Tianrui Jia, Chuan Shi:
Temporal Heterogeneous Interaction Graph Embedding for Next-Item Recommendation. 314-329 - Cheng Zheng, Bo Zong, Wei Cheng, Dongjin Song, Jingchao Ni, Wenchao Yu, Haifeng Chen, Wei Wang:
Node Classification in Temporal Graphs Through Stochastic Sparsification and Temporal Structural Convolution. 330-346 - Chunyuan Yuan, Jiacheng Li, Wei Zhou, Yijun Lu, Xiaodan Zhang, Songlin Hu:
DyHGCN: A Dynamic Heterogeneous Graph Convolutional Network to Learn Users' Dynamic Preferences for Information Diffusion Prediction. 347-363 - Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung:
A Self-attention Network Based Node Embedding Model. 364-377 - Donghan Yu, Ruohong Zhang, Zhengbao Jiang, Yuexin Wu, Yiming Yang:
Graph-Revised Convolutional Network. 378-393 - Hongwei Jin, Xinhua Zhang:
Robust Training of Graph Convolutional Networks via Latent Perturbation. 394-411 - Lingwei Chen, Xiaoting Li, Dinghao Wu:
Enhancing Robustness of Graph Convolutional Networks via Dropping Graph Connections. 412-428
Gaussian Processes
- Victor Picheny, Vincent Dutordoir, Artem Artemev, Nicolas Durrande:
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation. 431-446 - Henry B. Moss, David S. Leslie, Paul Rayson:
MUMBO: MUlti-task Max-Value Bayesian Optimization. 447-462 - Diederik M. Roijers, Luisa M. Zintgraf, Pieter Libin, Mathieu Reymond, Eugenio Bargiacchi, Ann Nowé:
Interactive Multi-objective Reinforcement Learning in Multi-armed Bandits with Gaussian Process Utility Models. 463-478 - Gonzalo Hernández-Muñoz, Carlos Villacampa-Calvo, Daniel Hernández-Lobato:
Deep Gaussian Processes Using Expectation Propagation and Monte Carlo Methods. 479-494
Computer Vision and Image Processing
- Yingcheng Su, Yichao Wu, Zhenmao Li, Qiushan Guo, Ken Chen, Junjie Yan, Ding Liang, Xiaolin Hu:
Companion Guided Soft Margin for Face Recognition. 497-514 - Manliang Cao, Xiangdong Zhou, Lan Lin:
Soft Labels Transfer with Discriminative Representations Learning for Unsupervised Domain Adaptation. 515-530 - Andrey Zhmoginov, Ian Fischer, Mark Sandler:
Information-Bottleneck Approach to Salient Region Discovery. 531-546 - Lu Chen, Jiao Sun, Wei Xu:
FAWA: Fast Adversarial Watermark Attack on Optical Character Recognition (OCR) Systems. 547-563
Natural Language Processing
- Shiwei Zhang, Xiuzhen Zhang, Jey Han Lau, Jeffrey Chan, Cécile Paris:
Less Is More: Rejecting Unreliable Reviews for Product Question Answering. 567-583 - Haitian Yang, Weiqing Huang, Xuan Zhao, Yan Wang, Yuyan Chen, Bin Lv, Rui Mao, Ning Li:
AMQAN: Adaptive Multi-Attention Question-Answer Networks for Answer Selection. 584-599 - Junyang Chen, Zhiguo Gong, Wei Wang, Xiao Dong, Wei Wang, Weiwen Liu, Cong Wang, Xian Chen:
Inductive Document Representation Learning for Short Text Clustering. 600-616 - Wanzheng Zhu, Hongyu Gong, Jiaming Shen, Chao Zhang, Jingbo Shang, Suma Bhat, Jiawei Han:
FUSE: Multi-faceted Set Expansion by Coherent Clustering of Skip-Grams. 617-632 - Lingwei Wei, Dou Hu, Wei Zhou, Xuehai Tang, Xiaodan Zhang, Xin Wang, Jizhong Han, Songlin Hu:
Hierarchical Interaction Networks with Rethinking Mechanism for Document-Level Sentiment Analysis. 633-649 - Kai Shu, Guoqing Zheng, Yichuan Li, Subhabrata Mukherjee, Ahmed Hassan Awadallah, Scott W. Ruston, Huan Liu:
Early Detection of Fake News with Multi-source Weak Social Supervision. 650-666 - Wenxin Hu, Xiaofeng Zhang, Yunpeng Ren:
Generating Financial Reports from Macro News via Multiple Edits Neural Networks. 667-682 - Zixuan Ke, Bing Liu, Hao Wang, Lei Shu:
Continual Learning with Knowledge Transfer for Sentiment Classification. 683-698
Bioinformatics
- Bruna Zamith Santos, Felipe Kenji Nakano, Ricardo Cerri, Celine Vens:
Predictive Bi-clustering Trees for Hierarchical Multi-label Classification. 701-718 - Xueping Peng, Guodong Long, Tao Shen, Sen Wang, Jing Jiang:
Self-attention Enhanced Patient Journey Understanding in Healthcare System. 719-735 - Ziyu Jia, Youfang Lin, Jing Wang, Kaixin Yang, Tianhang Liu, Xinwang Zhang:
MMCNN: A Multi-branch Multi-scale Convolutional Neural Network for Motor Imagery Classification. 736-751
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