Machine Learning and Knowledge Discovery in Databases. Research Track

Table of Contents

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   Book Overview
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   Chapter 1 Routine Bandits: Minimizing Regret on Recurring Problems
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   Chapter 2 Conservative Online Convex Optimization
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   Chapter 3 Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits
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   Chapter 4 Exploiting History Data for Nonstationary Multi-armed Bandit
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   Chapter 5 High-Probability Kernel Alignment Regret Bounds for Online Kernel Selection
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   Chapter 6 Periodic Intra-ensemble Knowledge Distillation for Reinforcement Learning
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   Chapter 7 Learning to Build High-Fidelity and Robust Environment Models
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   Chapter 8 Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement Learning
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    Chapter 9 Multi-agent Imitation Learning with Copulas
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    Chapter 10 CMIX: Deep Multi-agent Reinforcement Learning with Peak and Average Constraints
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    Chapter 11 Model-Based Offline Policy Optimization with Distribution Correcting Regularization
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    Chapter 12 Disagreement Options: Task Adaptation Through Temporally Extended Actions
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    Chapter 13 Deep Adaptive Multi-intention Inverse Reinforcement Learning
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    Chapter 14 Unsupervised Task Clustering for Multi-task Reinforcement Learning
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    Chapter 15 Deep Model Compression via Two-Stage Deep Reinforcement Learning
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    Chapter 16 Dropout’s Dream Land: Generalization from Learned Simulators to Reality
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    Chapter 17 Goal Modelling for Deep Reinforcement Learning Agents
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    Chapter 18 Deviation-Based Marked Temporal Point Process for Marker Prediction
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    Chapter 19 Deep Structural Point Process for Learning Temporal Interaction Networks
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    Chapter 20 Holistic Prediction for Public Transport Crowd Flows: A Spatio Dynamic Graph Network Approach
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    Chapter 21 Reservoir Pattern Sampling in Data Streams
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    Chapter 22 Discovering Proper Neighbors to Improve Session-Based Recommendation
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    Chapter 23 Continuous-Time Markov-Switching GARCH Process with Robust State Path Identification and Volatility Estimation
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    Chapter 24 Dynamic Heterogeneous Graph Embedding via Heterogeneous Hawkes Process
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    Chapter 25 Explainable Online Deep Neural Network Selection Using Adaptive Saliency Maps for Time Series Forecasting
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    Chapter 26 Change Detection in Multivariate Datastreams Controlling False Alarms
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    Chapter 27 Approximation Algorithms for Confidence Bands for Time Series
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    Chapter 28 A Mixed Noise and Constraint-Based Approach to Causal Inference in Time Series
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    Chapter 29 Estimating the Electrical Power Output of Industrial Devices with End-to-End Time-Series Classification in the Presence of Label Noise
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    Chapter 30 Multi-task Learning Curve Forecasting Across Hyperparameter Configurations and Datasets
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    Chapter 31 Streaming Decision Trees for Lifelong Learning
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    Chapter 32 Unifying Domain Adaptation and Domain Generalization for Robust Prediction Across Minority Racial Groups
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    Chapter 33 Deep Multi-task Augmented Feature Learning via Hierarchical Graph Neural Network
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    Chapter 34 Bridging Few-Shot Learning and Adaptation: New Challenges of Support-Query Shift
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    Chapter 35 Source Hypothesis Transfer for Zero-Shot Domain Adaptation
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    Chapter 36 FedPHP: Federated Personalization with Inherited Private Models
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    Chapter 37 Rumour Detection via Zero-Shot Cross-Lingual Transfer Learning
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    Chapter 38 Continual Learning with Dual Regularizations
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    Chapter 39 EARLIN: Early Out-of-Distribution Detection for Resource-Efficient Collaborative Inference
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    Chapter 40 LSMI-Sinkhorn: Semi-supervised Mutual Information Estimation with Optimal Transport
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    Chapter 41 Spatial Contrastive Learning for Few-Shot Classification
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    Chapter 42 Ensemble of Local Decision Trees for Anomaly Detection in Mixed Data
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    Chapter 43 Optimal Teaching Curricula with Compositional Simplicity Priors
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    Chapter 44 FedDNA: Federated Learning with Decoupled Normalization-Layer Aggregation for Non-IID Data
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    Chapter 45 The Curious Case of Convex Neural Networks
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    Chapter 46 UCSL : A Machine Learning Expectation-Maximization Framework for Unsupervised Clustering Driven by Supervised Learning
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    Chapter 47 Efficient and Less Centralized Federated Learning
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    Chapter 48 Topological Anomaly Detection in Dynamic Multilayer Blockchain Networks