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Machine Learning and Knowledge Discovery in Databases

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Cover of 'Machine Learning and Knowledge Discovery in Databases'

Table of Contents

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    Book Overview
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    Chapter 1 DEvIANT: Discovering Significant Exceptional (Dis-)Agreement Within Groups
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    Chapter 2 Maximal Closed Set and Half-Space Separations in Finite Closure Systems
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    Chapter 3 Sets of Robust Rules, and How to Find Them
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    Chapter 4 A Framework for Deep Constrained Clustering - Algorithms and Advances
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    Chapter 5 A Framework for Parallelizing Hierarchical Clustering Methods
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    Chapter 6 Unsupervised and Active Learning Using Maximin-Based Anomaly Detection
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    Chapter 7 The Elliptical Basis Function Data Descriptor (EBFDD) Network: A One-Class Classification Approach to Anomaly Detection
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    Chapter 8 Heavy-Tailed Kernels Reveal a Finer Cluster Structure in t-SNE Visualisations
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    Chapter 9 Uncovering Hidden Block Structure for Clustering
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    Chapter 10 CatchCore : Catching Hierarchical Dense Subtensor
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    Chapter 11 Fast and Parallelizable Ranking with Outliers from Pairwise Comparisons
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    Chapter 12 Black Box Explanation by Learning Image Exemplars in the Latent Feature Space
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    Chapter 13 Robust Anomaly Detection in Images Using Adversarial Autoencoders
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    Chapter 14 Holistic Assessment of Structure Discovery Capabilities of Clustering Algorithms
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    Chapter 15 Pattern-Based Anomaly Detection in Mixed-Type Time Series
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    Chapter 16 k Is the Magic Number—Inferring the Number of Clusters Through Nonparametric Concentration Inequalities
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    Chapter 17 From Abstract Items to Latent Spaces to Observed Data and Back: Compositional Variational Auto-Encoder
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    Chapter 18 Joint Multi-source Reduction
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    Chapter 19 Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold
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    Chapter 20 On the Stability of Feature Selection in the Presence of Feature Correlations
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    Chapter 21 Agnostic Feature Selection
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    Chapter 22 User-Guided Clustering in Heterogeneous Information Networks via Motif-Based Comprehensive Transcription
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    Chapter 23 Novel Dense Subgraph Discovery Primitives: Risk Aversion and Exclusion Queries
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    Chapter 24 Node Representation Learning for Directed Graphs
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    Chapter 25 Link Prediction via Higher-Order Motif Features
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    Chapter 26 SoRecGAT: Leveraging Graph Attention Mechanism for Top-N Social Recommendation
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    Chapter 27 Graph Signal Processing for Directed Graphs Based on the Hermitian Laplacian
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    Chapter 28 Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification
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    Chapter 29 node2bits : Compact Time- and Attribute-Aware Node Representations for User Stitching
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    Chapter 30 A Soft Affiliation Graph Model for Scalable Overlapping Community Detection
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    Chapter 31 Node Classification for Signed Social Networks Using Diffuse Interface Methods
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    Chapter 32 Triangle Completion Time Prediction Using Time-Conserving Embedding
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    Chapter 33 Adjustment Criteria for Recovering Causal Effects from Missing Data
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    Chapter 34 An Algorithm for Reducing the Number of Distinct Branching Conditions in a Decision Forest
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    Chapter 35 Fast Gradient Boosting Decision Trees with Bit-Level Data Structures
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    Chapter 36 Shrinkage Estimators for Uplift Regression
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    Chapter 37 String Sanitization: A Combinatorial Approach
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    Chapter 38 Online Linear Models for Edge Computing
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    Chapter 39 Fast Likelihood-Based Change Point Detection
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    Chapter 40 A Drift-Based Dynamic Ensemble Members Selection Using Clustering for Time Series Forecasting
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    Chapter 41 A Differentially Private Kernel Two-Sample Test
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    Chapter 42 Learning to Signal in the Goldilocks Zone: Improving Adversary Compliance in Security Games
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    Chapter 43 A Stochastic Quasi-Newton Method with Nesterov’s Accelerated Gradient
Attention for Chapter 17: From Abstract Items to Latent Spaces to Observed Data and Back: Compositional Variational Auto-Encoder
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Chapter title
From Abstract Items to Latent Spaces to Observed Data and Back: Compositional Variational Auto-Encoder
Chapter number 17
Book title
Machine Learning and Knowledge Discovery in Databases
Published by
Springer, Cham, September 2019
DOI 10.1007/978-3-030-46150-8_17
Book ISBNs
978-3-03-046149-2, 978-3-03-046150-8

Victor Berger, Michèle Sebag, Berger, Victor, Sebag, Michèle

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 43%
Researcher 3 21%
Student > Master 2 14%
Student > Doctoral Student 1 7%
Lecturer 1 7%
Other 0 0%
Unknown 1 7%
Readers by discipline Count As %
Computer Science 7 50%
Biochemistry, Genetics and Molecular Biology 1 7%
Agricultural and Biological Sciences 1 7%
Mathematics 1 7%
Physics and Astronomy 1 7%
Other 1 7%
Unknown 2 14%