Machine Learning and Knowledge Discovery in Databases

Table of Contents

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   Book Overview
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   Chapter 1 Temporally Evolving Community Detection and Prediction in Content-Centric Networks
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   Chapter 2 Local Topological Data Analysis to Uncover the Global Structure of Data Approaching Graph-Structured Topologies
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   Chapter 3 Similarity Modeling on Heterogeneous Networks via Automatic Path Discovery
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   Chapter 4 Dynamic Hierarchies in Temporal Directed Networks
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   Chapter 5 Risk-Averse Matchings over Uncertain Graph Databases
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   Chapter 6 Discovering Urban Travel Demands Through Dynamic Zone Correlation in Location-Based Social Networks
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   Chapter 7 Social-Affiliation Networks: Patterns and the SOAR Model
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   Chapter 8 ONE-M : Modeling the Co-evolution of Opinions and Network Connections
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    Chapter 9 Think Before You Discard: Accurate Triangle Counting in Graph Streams with Deletions
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    Chapter 10 Semi-supervised Blockmodelling with Pairwise Guidance
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    Chapter 11 Large-Scale Nonlinear Variable Selection via Kernel Random Features
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    Chapter 12 Fast and Provably Effective Multi-view Classification with Landmark-Based SVM
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    Chapter 13 Nyström-SGD: Fast Learning of Kernel-Classifiers with Conditioned Stochastic Gradient Descent
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    Chapter 14 Hyperparameter Learning for Conditional Kernel Mean Embeddings with Rademacher Complexity Bounds
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    Chapter 15 Deep Learning Architecture Search by Neuro-Cell-Based Evolution with Function-Preserving Mutations
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    Chapter 16 VC-Dimension Based Generalization Bounds for Relational Learning
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    Chapter 17 Robust Super-Level Set Estimation Using Gaussian Processes
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    Chapter 18 Scalable Nonlinear AUC Maximization Methods
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    Chapter 19 Lambert Matrix Factorization
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    Chapter 20 Identifying and Alleviating Concept Drift in Streaming Tensor Decomposition
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    Chapter 21 MASAGA: A Linearly-Convergent Stochastic First-Order Method for Optimization on Manifolds
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    Chapter 22 Block CUR: Decomposing Matrices Using Groups of Columns
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    Chapter 23 $$\mathtt{SpectralLeader}$$ : Online Spectral Learning for Single Topic Models
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    Chapter 24 Online Learning of Weighted Relational Rules for Complex Event Recognition
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    Chapter 25 Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees
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    Chapter 26 Online Feature Selection by Adaptive Sub-gradient Methods
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    Chapter 27 Frame-Based Optimal Design
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    Chapter 28 Hierarchical Active Learning with Proportion Feedback on Regions
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    Chapter 29 An Efficient Algorithm for Computing Entropic Measures of Feature Subsets
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    Chapter 30 Anytime Subgroup Discovery in Numerical Domains with Guarantees
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    Chapter 31 Discovering Spatio-Temporal Latent Influence in Geographical Attention Dynamics
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    Chapter 32 Mining Periodic Patterns with a MDL Criterion
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    Chapter 33 Revisiting Conditional Functional Dependency Discovery: Splitting the “C” from the “FD”
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    Chapter 34 Sqn2Vec: Learning Sequence Representation via Sequential Patterns with a Gap Constraint
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    Chapter 35 Mining Tree Patterns with Partially Injective Homomorphisms
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    Chapter 36 Variational Bayes for Mixture Models with Censored Data
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    Chapter 37 Exploration Enhanced Expected Improvement for Bayesian Optimization
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    Chapter 38 A Left-to-Right Algorithm for Likelihood Estimation in Gamma-Poisson Factor Analysis
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    Chapter 39 Causal Inference on Multivariate and Mixed-Type Data
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    Chapter 40 POLAR: Attention-Based CNN for One-Shot Personalized Article Recommendation
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    Chapter 41 Learning Multi-granularity Dynamic Network Representations for Social Recommendation
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    Chapter 42 GeoDCF: Deep Collaborative Filtering with Multifaceted Contextual Information in Location-Based Social Networks
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    Chapter 43 Personalized Thread Recommendation for MOOC Discussion Forums
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    Chapter 44 Inferring Continuous Latent Preference on Transition Intervals for Next Point-of-Interest Recommendation
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    Chapter 45 Feature Selection for Unsupervised Domain Adaptation Using Optimal Transport
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    Chapter 46 Web-Induced Heterogeneous Transfer Learning with Sample Selection
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    Chapter 47 Towards More Reliable Transfer Learning
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    Chapter 48 Differentially Private Hypothesis Transfer Learning
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    Chapter 49 Information-Theoretic Transfer Learning Framework for Bayesian Optimisation
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    Chapter 50 A Unified Framework for Domain Adaptation Using Metric Learning on Manifolds