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ORCIDPKDD / ECML 2018: Dublin, Ireland
- Michele Berlingerio, Francesco Bonchi, Thomas Gärtner, Neil Hurley, Georgiana Ifrim
:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part II. Lecture Notes in Computer Science 11052, Springer 2019, ISBN 978-3-030-10927-1
Graphs
- Ana Paula Appel, Renato Luiz de Freitas Cunha, Charu C. Aggarwal, Marcela Megumi Terakado:
Temporally Evolving Community Detection and Prediction in Content-Centric Networks. 3-18 - Robin Vandaele, Tijl De Bie, Yvan Saeys:
Local Topological Data Analysis to Uncover the Global Structure of Data Approaching Graph-Structured Topologies. 19-36 - Carl Yang, Mengxiong Liu, Frank He, Xikun Zhang, Jian Peng, Jiawei Han:
Similarity Modeling on Heterogeneous Networks via Automatic Path Discovery. 37-54 - Nikolaj Tatti:
Dynamic Hierarchies in Temporal Directed Networks. 55-70 - Charalampos E. Tsourakakis, Shreyas Sekar, Johnson Lam, Liu Yang:
Risk-Averse Matchings over Uncertain Graph Databases. 71-87 - Wangsu Hu, Zijun Yao, Sen Yang, Shuhong Chen, Peter Jing Jin:
Discovering Urban Travel Demands Through Dynamic Zone Correlation in Location-Based Social Networks. 88-104 - Dhivya Eswaran, Reihaneh Rabbany, Artur W. Dubrawski, Christos Faloutsos:
Social-Affiliation Networks: Patterns and the SOAR Model. 105-121 - Aastha Nigam, Kijung Shin, Ashwin Bahulkar, Bryan Hooi, David Hachen, Boleslaw K. Szymanski, Christos Faloutsos, Nitesh V. Chawla:
ONE-M: Modeling the Co-evolution of Opinions and Network Connections. 122-140 - Kijung Shin, Jisu Kim, Bryan Hooi, Christos Faloutsos:
Think Before You Discard: Accurate Triangle Counting in Graph Streams with Deletions. 141-157 - Mohadeseh Ganji, Jeffrey Chan, Peter J. Stuckey, James Bailey, Christopher Leckie, Kotagiri Ramamohanarao, Laurence A. F. Park:
Semi-supervised Blockmodelling with Pairwise Guidance. 158-174
Kernel Methods
- Magda Gregorová, Jason Ramapuram, Alexandros Kalousis, Stéphane Marchand-Maillet:
Large-Scale Nonlinear Variable Selection via Kernel Random Features. 177-192 - Valentina Zantedeschi, Rémi Emonet, Marc Sebban:
Fast and Provably Effective Multi-view Classification with Landmark-Based SVM. 193-208 - Lukas Pfahler, Katharina Morik:
Nyström-SGD: Fast Learning of Kernel-Classifiers with Conditioned Stochastic Gradient Descent. 209-224
Learning Paradigms
- Kelvin Hsu, Richard Nock, Fabio Ramos:
Hyperparameter Learning for Conditional Kernel Mean Embeddings with Rademacher Complexity Bounds. 227-242 - Martin Wistuba:
Deep Learning Architecture Search by Neuro-Cell-Based Evolution with Function-Preserving Mutations. 243-258 - Ondrej Kuzelka, Yuyi Wang, Steven Schockaert:
VC-Dimension Based Generalization Bounds for Relational Learning. 259-275 - Andrea Zanette, Junzi Zhang, Mykel J. Kochenderfer:
Robust Super-Level Set Estimation Using Gaussian Processes. 276-291 - Majdi Khalid, Indrakshi Ray, Hamidreza Chitsaz:
Scalable Nonlinear AUC Maximization Methods. 292-307
Matrix and Tensor Analysis
- Arto Klami, Jarkko Lagus, Joseph Sakaya:
Lambert Matrix Factorization. 311-326 - Ravdeep Pasricha, Ekta Gujral, Evangelos E. Papalexakis:
Identifying and Alleviating Concept Drift in Streaming Tensor Decomposition. 327-343 - Reza Babanezhad, Issam H. Laradji, Alireza Shafaei, Mark Schmidt:
MASAGA: A Linearly-Convergent Stochastic First-Order Method for Optimization on Manifolds. 344-359 - Urvashi Oswal, Swayambhoo Jain, Kevin S. Xu, Brian Eriksson:
Block CUR: Decomposing Matrices Using Groups of Columns. 360-376
Online and Active Learning
- Tong Yu, Branislav Kveton, Zheng Wen, Hung Bui, Ole J. Mengshoel:
SpectralLeader: Online Spectral Learning for Single Topic Models. 379-395 - Nikos Katzouris, Evangelos Michelioudakis, Alexander Artikis, Georgios Paliouras:
Online Learning of Weighted Relational Rules for Complex Event Recognition. 396-413 - Guiliang Liu, Oliver Schulte, Wang Zhu, Qingcan Li:
Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees. 414-429 - Tingting Zhai, Hao Wang, Frédéric Koriche, Yang Gao:
Online Feature Selection by Adaptive Sub-gradient Methods. 430-446 - Sebastian Mair, Yannick Rudolph, Vanessa Closius, Ulf Brefeld:
Frame-Based Optimal Design. 447-463 - Zhipeng Luo, Milos Hauskrecht:
Hierarchical Active Learning with Proportion Feedback on Regions. 464-480
Pattern and Sequence Mining
- Frédéric Pennerath:
An Efficient Algorithm for Computing Entropic Measures of Feature Subsets. 483-499 - Aimene Belfodil, Adnene Belfodil, Mehdi Kaytoue:
Anytime Subgroup Discovery in Numerical Domains with Guarantees. 500-516 - Minoru Higuchi, Kanji Matsutani, Masahito Kumano, Masahiro Kimura:
Discovering Spatio-Temporal Latent Influence in Geographical Attention Dynamics. 517-534 - Esther Galbrun, Peggy Cellier, Nikolaj Tatti, Alexandre Termier, Bruno Crémilleux:
Mining Periodic Patterns with a MDL Criterion. 535-551 - Joeri Rammelaere, Floris Geerts:
Revisiting Conditional Functional Dependency Discovery: Splitting the "C" from the "FD". 552-568 - Dang Nguyen, Wei Luo, Tu Dinh Nguyen, Svetha Venkatesh, Dinh Q. Phung:
Sqn2Vec: Learning Sequence Representation via Sequential Patterns with a Gap Constraint. 569-584 - Till Hendrik Schulz, Tamás Horváth, Pascal Welke, Stefan Wrobel:
Mining Tree Patterns with Partially Injective Homomorphisms. 585-601
Probabilistic Models and Statistical Methods
- Masahiro Kohjima, Tatsushi Matsubayashi, Hiroyuki Toda:
Variational Bayes for Mixture Models with Censored Data. 605-620 - Julian Berk, Vu Nguyen, Sunil Gupta, Santu Rana, Svetha Venkatesh:
Exploration Enhanced Expected Improvement for Bayesian Optimization. 621-637 - Joan Capdevila, Jesús Cerquides, Jordi Torres, François Petitjean, Wray L. Buntine:
A Left-to-Right Algorithm for Likelihood Estimation in Gamma-Poisson Factor Analysis. 638-654 - Alexander Marx, Jilles Vreeken:
Causal Inference on Multivariate and Mixed-Type Data. 655-671
Recommender Systems
- Zhengxiao Du, Jie Tang, Yuhui Ding:
POLAR: Attention-Based CNN for One-Shot Personalized Article Recommendation. 675-690 - Peng Liu, Lemei Zhang, Jon Atle Gulla:
Learning Multi-granularity Dynamic Network Representations for Social Recommendation. 691-708 - Dimitrios Rafailidis, Fabio Crestani:
GeoDCF: Deep Collaborative Filtering with Multifaceted Contextual Information in Location-Based Social Networks. 709-724 - Andrew S. Lan, Jonathan C. Spencer, Ziqi Chen, Christopher G. Brinton, Mung Chiang:
Personalized Thread Recommendation for MOOC Discussion Forums. 725-740 - Jing He, Xin Li, Lejian Liao, Mingzhong Wang:
Inferring Continuous Latent Preference on Transition Intervals for Next Point-of-Interest Recommendation. 741-756
Transfer Learning
- Léo Gautheron, Ievgen Redko, Carole Lartizien:
Feature Selection for Unsupervised Domain Adaptation Using Optimal Transport. 759-776 - Sanatan Sukhija, Narayanan Chatapuram Krishnan:
Web-Induced Heterogeneous Transfer Learning with Sample Selection. 777-793 - Zirui Wang, Jaime G. Carbonell:
Towards More Reliable Transfer Learning. 794-810 - Yang Wang, Quanquan Gu, Donald E. Brown:
Differentially Private Hypothesis Transfer Learning. 811-826 - Anil Ramachandran, Sunil Gupta, Santu Rana, Svetha Venkatesh:
Information-Theoretic Transfer Learning Framework for Bayesian Optimisation. 827-842 - Sridhar Mahadevan, Bamdev Mishra, Shalini Ghosh:
A Unified Framework for Domain Adaptation Using Metric Learning on Manifolds. 843-860
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