Machine Learning and Knowledge Discovery in Databases

Table of Contents

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   Book Overview
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   Chapter 1 The ABC of Data: A Classifying Framework for Data Readiness
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   Chapter 2 Automating Common Data Science Matrix Transformations
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   Chapter 3 DeepNotebooks: Deep Probabilistic Models Construct Python Notebooks for Reporting Datasets
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   Chapter 4 HyperUCB: Hyperparameter Optimization Using Contextual Bandits
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   Chapter 5 Learning Parsers for Technical Drawings
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   Chapter 6 Meta-learning of Textual Representations
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   Chapter 7 ReinBo: Machine Learning Pipeline Conditional Hierarchy Search and Configuration with Bayesian Optimization Embedded Reinforcement Learning
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   Chapter 8 Supervised Human-Guided Data Exploration
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    Chapter 9 SynthLog: A Language for Synthesising Inductive Data Models (Extended Abstract)
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    Chapter 10 The autofeat Python Library for Automated Feature Engineering and Selection
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    Chapter 11 The Extended Dawid-Skene Model
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    Chapter 12 Towards Automated Configuration of Stream Clustering Algorithms
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    Chapter 13 Effect of Superpixel Aggregation on Explanations in LIME – A Case Study with Biological Data
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    Chapter 14 Global Explanations with Local Scoring
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    Chapter 15 Adversarial Robustness Curves
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    Chapter 16 Enriching Visual with Verbal Explanations for Relational Concepts – Combining LIME with Aleph
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    Chapter 17 Quantifying Model Complexity via Functional Decomposition for Better Post-hoc Interpretability
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    Chapter 18 Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations
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    Chapter 19 Learning and Interpreting Potentials for Classical Hamiltonian Systems
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    Chapter 20 Finding Interpretable Concept Spaces in Node Embeddings Using Knowledge Bases
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    Chapter 21 Local Interpretation Methods to Machine Learning Using the Domain of the Feature Space
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    Chapter 22 Measuring Unfairness Through Game-Theoretic Interpretability
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    Chapter 23 LioNets: Local Interpretation of Neural Networks Through Penultimate Layer Decoding
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    Chapter 24 Distributed Generative Modelling with Sub-linear Communication Overhead
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    Chapter 25 Distributed Learning of Neural Networks with One Round of Communication
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    Chapter 26 Decentralized Learning with Budgeted Network Load Using Gaussian Copulas and Classifier Ensembles
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    Chapter 27 Decentralized Recommendation Based on Matrix Factorization: A Comparison of Gossip and Federated Learning
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    Chapter 28 Ring-Star : A Sparse Topology for Faster Model Averaging in Decentralized Parallel SGD
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    Chapter 29 Hardware Acceleration of Machine Learning Beyond Linear Algebra
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    Chapter 30 Detecting Stable Communities in Link Streams at Multiple Temporal Scales
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    Chapter 31 A Comparative Study of Community Detection Techniques for Large Evolving Graphs
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    Chapter 32 Dynamic Joint Variational Graph Autoencoders
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    Chapter 33 Evolution Analysis of Large Graphs with Gradoop
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    Chapter 34 MHDNE: Network Embedding Based on Multivariate Hawkes Process
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    Chapter 35 Multimodal Deep Networks for Text and Image-Based Document Classification
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    Chapter 36 Manifold Mixing for Stacked Regularization
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    Chapter 37 A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data
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    Chapter 38 Deep Generative Multi-view Learning
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    Chapter 39 SDE-KG: A Stochastic Dynamic Environment for Knowledge Graphs
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    Chapter 40 Iterative Representation Learning for Entity Alignment Leveraging Textual Information
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    Chapter 41 #MeTooMaastricht: Building a Chatbot to Assist Survivors of Sexual Harassment
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    Chapter 42 Analysis of Vocational Education and Training and the Labour Market in Catalonia. A Data-Driven Approach
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    Chapter 43 SOS-EW: System for Overdose Spike Early Warning Using Drug Mover’s Distance-Based Hawkes Processes
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    Chapter 44 Improving GP-UCB Algorithm by Harnessing Decomposed Feedback
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    Chapter 45 Optimizing Waste Collection: A Data Mining Approach
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    Chapter 46 Mobile Game Theory with Street Gangs
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    Chapter 47 Paired-Consistency: An Example-Based Model-Agnostic Approach to Fairness Regularization in Machine Learning
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    Chapter 48 Transferring Clinical Prediction Models Across Hospitals and Electronic Health Record Systems
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    Chapter 49 Linking Physicians to Medical Research Results via Knowledge Graph Embeddings and Twitter
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    Chapter 50 Prediction of Frequent Out-Of-Hours’ Medical Use
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    Chapter 51 Forecast of Study Success in the STEM Disciplines Based Solely on Academic Records
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    Chapter 52 Improving Access to Science for Social Good