Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part I
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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.
Adversarial Learning.- Image Anomaly Detection with Generative Adversarial Networks.- Image-to-Markup Generation via Paired Adversarial Learning.- Toward an Understanding of Adversarial Examples in Clinical Trials.- ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector.- Anomaly and Outlier Detection.- GridWatch: Sensor Placement and Anomaly Detection in the Electrical Grid.- Incorporating Privileged Information to Unsupervised Anomaly Detection.- L1-Depth Revisited: A Robust Angle-based Outlier Factor in High-dimensional Space.- Beyond Outlier Detection: LookOut for Pictorial Explanation.- Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier Features.- Group Anomaly Detection using Deep Generative Models.- Applications.- A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements.- Face-Cap: Image Captioning using Facial Expression Analysis.- Pedestrian Trajectory Prediction with Structured Memory Hierarchies.- Classification.- Multiple Instance Learning with Bag-level Randomized Trees.- One-class Quantification.- Deep F-Measure Maximization in Multi-Label Classification: A Comparative Study.- Ordinal Label Proportions.- AWX: An Integrated Approach to Hierarchical-Multilabel Classification.- Clustering and Unsupervised Learning.- Clustering in the Presence of Concept Drift.- Time Warp Invariant Dictionary Learning for Time Series Clustering.- How Your Supporters and Opponents Define Your Interestingness.- Deep Learning.- Efficient Decentralized Deep Learning by Dynamic Model Averaging.- Using Supervised Pretraining to Improve Generalization of Neural Networks on Binary Classification Problems.- Towards Efficient Forward Propagation on Resource-Constrained Systems.- Auxiliary Guided Autoregressive Variational Autoencoders.- Cooperative Multi-Agent Policy Gradient.- Parametric t-Distributed Stochastic Exemplar-centered Embedding.- Joint autoencoders: a flexible meta-learning framework.- Privacy Preserving Synthetic Data Release Using Deep Learning.- On Finer Control of Information Flow in LSTMs.- MaxGain: Regularisation of Neural Networks by Constraining Activation Magnitudes.- Ontology alignment based on word embedding and random forest classification.- Domain Adaption in One-Shot Learning.- Ensemble Methods.- Axiomatic Characterization of AdaBoost and the Multiplicative Weight Update Procedure.- Modular Dimensionality Reduction.- Constructive Aggregation and its Application to Forecasting with Dynamic Ensembles.- MetaBags: Bagged Meta-Decision Trees for Regression.- Evaluation.- Visualizing the Feature Importance for Black Box Models.- Efficient estimation of AUC in a sliding window.- Controlling and visualizing the precision-recall tradeoff for external performance indices.- Evaluation Procedures for Forecasting with Spatio-Temporal Data.- A Blended Metric for Multi-label Optimisation and Evaluation.