Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part II

Description

The three volume proceedings LNAI 11051 – 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, held in Dublin, Ireland, in September 2018. The total of 131 regular papers presented in part I and part II was carefully reviewed and selected from 535 submissions; there are 52 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows: Part I: adversarial learning; anomaly and outlier detection; applications; classification; clustering and unsupervised learning; deep learningensemble methods; and evaluation. Part II: graphs; kernel methods; learning paradigms; matrix and tensor analysis; online and active learning; pattern and sequence mining; probabilistic models and statistical methods; recommender systems; and transfer learning. Part III: ADS data science applications; ADS e-commerce; ADS engineering and design; ADS financial and security; ADS health; ADS sensing and positioning; nectar track; and demo track. Front Matter ....Pages I-XXX Front Matter ....Pages 1-1 Temporally Evolving Community Detection and Prediction in Content-Centric Networks (Ana Paula Appel, Renato L. F. Cunha, Charu C. Aggarwal, Marcela Megumi Terakado)....Pages 3-18 Local Topological Data Analysis to Uncover the Global Structure of Data Approaching Graph-Structured Topologies (Robin Vandaele, Tijl De Bie, Yvan Saeys)....Pages 19-36 Similarity Modeling on Heterogeneous Networks via Automatic Path Discovery (Carl Yang, Mengxiong Liu, Frank He, Xikun Zhang, Jian Peng, Jiawei Han)....Pages 37-54 Dynamic Hierarchies in Temporal Directed Networks (Nikolaj Tatti)....Pages 55-70 Risk-Averse Matchings over Uncertain Graph Databases (Charalampos E. Tsourakakis, Shreyas Sekar, Johnson Lam, Liu Yang)....Pages 71-87 Discovering Urban Travel Demands Through Dynamic Zone Correlation in Location-Based Social Networks (Wangsu Hu, Zijun Yao, Sen Yang, Shuhong Chen, Peter J. Jin)....Pages 88-104 Social-Affiliation Networks: Patterns and the SOAR Model (Dhivya Eswaran, Reihaneh Rabbany, Artur W. Dubrawski, Christos Faloutsos)....Pages 105-121 ONE-M: Modeling the Co-evolution of Opinions and Network Connections (Aastha Nigam, Kijung Shin, Ashwin Bahulkar, Bryan Hooi, David Hachen, Boleslaw K. Szymanski et al.)....Pages 122-140 Think Before You Discard: Accurate Triangle Counting in Graph Streams with Deletions (Kijung Shin, Jisu Kim, Bryan Hooi, Christos Faloutsos)....Pages 141-157 Semi-supervised Blockmodelling with Pairwise Guidance (Mohadeseh Ganji, Jeffrey Chan, Peter J. Stuckey, James Bailey, Christopher Leckie, Kotagiri Ramamohanarao et al.)....Pages 158-174 Front Matter ....Pages 175-175 Large-Scale Nonlinear Variable Selection via Kernel Random Features (Magda Gregorová, Jason Ramapuram, Alexandros Kalousis, Stéphane Marchand-Maillet)....Pages 177-192 Fast and Provably Effective Multi-view Classification with Landmark-Based SVM (Valentina Zantedeschi, Rémi Emonet, Marc Sebban)....Pages 193-208 Nyström-SGD: Fast Learning of Kernel-Classifiers with Conditioned Stochastic Gradient Descent (Lukas Pfahler, Katharina Morik)....Pages 209-224 Front Matter ....Pages 225-225 Hyperparameter Learning for Conditional Kernel Mean Embeddings with Rademacher Complexity Bounds (Kelvin Hsu, Richard Nock, Fabio Ramos)....Pages 227-242 Deep Learning Architecture Search by Neuro-Cell-Based Evolution with Function-Preserving Mutations (Martin Wistuba)....Pages 243-258 VC-Dimension Based Generalization Bounds for Relational Learning (Ondřej Kuželka, Yuyi Wang, Steven Schockaert)....Pages 259-275 Robust Super-Level Set Estimation Using Gaussian Processes (Andrea Zanette, Junzi Zhang, Mykel J. Kochenderfer)....Pages 276-291 Scalable Nonlinear AUC Maximization Methods (Majdi Khalid, Indrakshi Ray, Hamidreza Chitsaz)....Pages 292-307 Front Matter ....Pages 309-309 Lambert Matrix Factorization (Arto Klami, Jarkko Lagus, Joseph Sakaya)....Pages 311-326 Identifying and Alleviating Concept Drift in Streaming Tensor Decomposition (Ravdeep Pasricha, Ekta Gujral, Evangelos E. Papalexakis)....Pages 327-343 MASAGA: A Linearly-Convergent Stochastic First-Order Method for Optimization on Manifolds (Reza Babanezhad, Issam H. Laradji, Alireza Shafaei, Mark Schmidt)....Pages 344-359 Block CUR: Decomposing Matrices Using Groups of Columns (Urvashi Oswal, Swayambhoo Jain, Kevin S. Xu, Brian Eriksson)....Pages 360-376 Front Matter ....Pages 377-377 \(\mathtt{SpectralLeader}\): Online Spectral Learning for Single Topic Models (Tong Yu, Branislav Kveton, Zheng Wen, Hung Bui, Ole J. Mengshoel)....Pages 379-395 Online Learning of Weighted Relational Rules for Complex Event Recognition (Nikos Katzouris, Evangelos Michelioudakis, Alexander Artikis, Georgios Paliouras)....Pages 396-413 Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees (Guiliang Liu, Oliver Schulte, Wang Zhu, Qingcan Li)....Pages 414-429 Online Feature Selection by Adaptive Sub-gradient Methods (Tingting Zhai, Hao Wang, Frédéric Koriche, Yang Gao)....Pages 430-446 Frame-Based Optimal Design (Sebastian Mair, Yannick Rudolph, Vanessa Closius, Ulf Brefeld)....Pages 447-463 Hierarchical Active Learning with Proportion Feedback on Regions (Zhipeng Luo, Milos Hauskrecht)....Pages 464-480 Front Matter ....Pages 481-481 An Efficient Algorithm for Computing Entropic Measures of Feature Subsets (Frédéric Pennerath)....Pages 483-499 Anytime Subgroup Discovery in Numerical Domains with Guarantees (Aimene Belfodil, Adnene Belfodil, Mehdi Kaytoue)....Pages 500-516 Discovering Spatio-Temporal Latent Influence in Geographical Attention Dynamics (Minoru Higuchi, Kanji Matsutani, Masahito Kumano, Masahiro Kimura)....Pages 517-534 Mining Periodic Patterns with a MDL Criterion (Esther Galbrun, Peggy Cellier, Nikolaj Tatti, Alexandre Termier, Bruno Crémilleux)....Pages 535-551 Revisiting Conditional Functional Dependency Discovery: Splitting the “C” from the “FD” (Joeri Rammelaere, Floris Geerts)....Pages 552-568 Sqn2Vec: Learning Sequence Representation via Sequential Patterns with a Gap Constraint (Dang Nguyen, Wei Luo, Tu Dinh Nguyen, Svetha Venkatesh, Dinh Phung)....Pages 569-584 Mining Tree Patterns with Partially Injective Homomorphisms (Till Hendrik Schulz, Tamás Horváth, Pascal Welke, Stefan Wrobel)....Pages 585-601 Front Matter ....Pages 603-603 Variational Bayes for Mixture Models with Censored Data (Masahiro Kohjima, Tatsushi Matsubayashi, Hiroyuki Toda)....Pages 605-620 Exploration Enhanced Expected Improvement for Bayesian Optimization (Julian Berk, Vu Nguyen, Sunil Gupta, Santu Rana, Svetha Venkatesh)....Pages 621-637 A Left-to-Right Algorithm for Likelihood Estimation in Gamma-Poisson Factor Analysis (Joan Capdevila, Jesús Cerquides, Jordi Torres, François Petitjean, Wray Buntine)....Pages 638-654 Causal Inference on Multivariate and Mixed-Type Data (Alexander Marx, Jilles Vreeken)....Pages 655-671 Front Matter ....Pages 673-673 POLAR: Attention-Based CNN for One-Shot Personalized Article Recommendation (Zhengxiao Du, Jie Tang, Yuhui Ding)....Pages 675-690 Learning Multi-granularity Dynamic Network Representations for Social Recommendation (Peng Liu, Lemei Zhang, Jon Atle Gulla)....Pages 691-708 GeoDCF: Deep Collaborative Filtering with Multifaceted Contextual Information in Location-Based Social Networks (Dimitrios Rafailidis, Fabio Crestani)....Pages 709-724 Personalized Thread Recommendation for MOOC Discussion Forums (Andrew S. Lan, Jonathan C. Spencer, Ziqi Chen, Christopher G. Brinton, Mung Chiang)....Pages 725-740 Inferring Continuous Latent Preference on Transition Intervals for Next Point-of-Interest Recommendation (Jing He, Xin Li, Lejian Liao, Mingzhong Wang)....Pages 741-756 Front Matter ....Pages 757-757 Feature Selection for Unsupervised Domain Adaptation Using Optimal Transport (Leo Gautheron, Ievgen Redko, Carole Lartizien)....Pages 759-776 Web-Induced Heterogeneous Transfer Learning with Sample Selection (Sanatan Sukhija, Narayanan C. Krishnan)....Pages 777-793 Towards More Reliable Transfer Learning (Zirui Wang, Jaime Carbonell)....Pages 794-810 Differentially Private Hypothesis Transfer Learning (Yang Wang, Quanquan Gu, Donald Brown)....Pages 811-826 Information-Theoretic Transfer Learning Framework for Bayesian Optimisation (Anil Ramachandran, Sunil Gupta, Santu Rana, Svetha Venkatesh)....Pages 827-842 A Unified Framework for Domain Adaptation Using Metric Learning on Manifolds (Sridhar Mahadevan, Bamdev Mishra, Shalini Ghosh)....Pages 843-860 Back Matter ....Pages 861-866