Machine Learning and Knowledge Discovery in Databases

Table of Contents

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   Book Overview
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   Chapter 1 Pass-Efficient Randomized SVD with Boosted Accuracy
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   Chapter 2 CDPS: Constrained DTW-Preserving Shapelets
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   Chapter 3 Structured Nonlinear Discriminant Analysis
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   Chapter 4 LSCALE: Latent Space Clustering-Based Active Learning for Node Classification
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   Chapter 5 Powershap: A Power-Full Shapley Feature Selection Method
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   Chapter 6 Automated Cancer Subtyping via Vector Quantization Mutual Information Maximization
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   Chapter 7 Wasserstein t -SNE
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   Chapter 8 Nonparametric Bayesian Deep Visualization
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    Chapter 9 FastDEC: Clustering by Fast Dominance Estimation
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    Chapter 10 SECLEDS: Sequence Clustering in Evolving Data Streams via Multiple Medoids and Medoid Voting
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    Chapter 11 Knowledge Integration in Deep Clustering
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    Chapter 12 ARES: Locally Adaptive Reconstruction-Based Anomaly Scoring
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    Chapter 13 R2-AD2: Detecting Anomalies by Analysing the Raw Gradient
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    Chapter 14 Hop-Count Based Self-supervised Anomaly Detection on Attributed Networks
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    Chapter 15 Deep Learning Based Urban Anomaly Prediction from Spatiotemporal Data
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    Chapter 16 Detecting Anomalies with Autoencoders on Data Streams
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    Chapter 17 Anomaly Detection via Few-Shot Learning on Normality
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    Chapter 18 Interpretations of Predictive Models for Lifestyle-related Diseases at Multiple Time Intervals
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    Chapter 19 Fair and Efficient Alternatives to Shapley-based Attribution Methods
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    Chapter 20 SMACE: A New Method for the Interpretability of Composite Decision Systems
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    Chapter 21 Calibrate to Interpret
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    Chapter 22 Knowledge-Driven Interpretation of Convolutional Neural Networks
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    Chapter 23 Neural Networks with Feature Attribution and Contrastive Explanations
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    Chapter 24 Explaining Predictions by Characteristic Rules
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    Chapter 25 Session-Based Recommendation Along with the Session Style of Explanation
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    Chapter 26 ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification
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    Chapter 27 VCNet: A Self-explaining Model for Realistic Counterfactual Generation
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    Chapter 28 A Recommendation System for CAD Assembly Modeling Based on Graph Neural Networks
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    Chapter 29 AD-AUG: Adversarial Data Augmentation for Counterfactual Recommendation
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    Chapter 30 Bi-directional Contrastive Distillation for Multi-behavior Recommendation
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    Chapter 31 Improving Micro-video Recommendation by Controlling Position Bias
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    Chapter 32 Mitigating Confounding Bias for Recommendation via Counterfactual Inference
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    Chapter 33 Recommending Related Products Using Graph Neural Networks in Directed Graphs
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    Chapter 34 A U-Shaped Hierarchical Recommender by Multi-resolution Collaborative Signal Modeling
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    Chapter 35 Basket Booster for Prototype-based Contrastive Learning in Next Basket Recommendation
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    Chapter 36 Graph Contrastive Learning with Adaptive Augmentation for Recommendation
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    Chapter 37 Multi-interest Extraction Joint with Contrastive Learning for News Recommendation
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    Chapter 38 On the Relationship Between Disentanglement and Multi-task Learning
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    Chapter 39 InCo: Intermediate Prototype Contrast for Unsupervised Domain Adaptation
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    Chapter 40 Fast and Accurate Importance Weighting for Correcting Sample Bias
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    Chapter 41 Overcoming Catastrophic Forgetting via Direction-Constrained Optimization
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    Chapter 42 Newer is Not Always Better: Rethinking Transferability Metrics, Their Peculiarities, Stability and Performance
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    Chapter 43 Learning to Teach Fairness-Aware Deep Multi-task Learning