Machine Learning and Knowledge Discovery in Databases. Research Track

Table of Contents

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   Book Overview
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   Chapter 1 Interpretable and Generalizable Spatiotemporal Predictive Learning with Disentangled Consistency
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   Chapter 2 Reinventing Node-centric Traffic Forecasting for Improved Accuracy and Efficiency
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   Chapter 3 Direct-Effect Risk Minimization for Domain Generalization
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   Chapter 4 Federated Frank-Wolfe Algorithm
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   Chapter 5 Bootstrap Latents of Nodes and Neighbors for Graph Self-supervised Learning
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   Chapter 6 Deep Sketched Output Kernel Regression for Structured Prediction
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   Chapter 7 Hyperbolic Delaunay Geometric Alignment
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   Chapter 8 ApmNet: Toward Generalizable Visual Continuous Control with Pre-trained Image Models
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    Chapter 9 AdaHAT: Adaptive Hard Attention to the Task in Task-Incremental Learning
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    Chapter 10 Probabilistic Circuits with Constraints via Convex Optimization
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    Chapter 11 FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification
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    Chapter 12 Selecting from Multiple Strategies Improves the Foreseeable Reasoning of Tool-Augmented Large Language Models
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    Chapter 13 Estimating Direct and Indirect Causal Effects of Spatiotemporal Interventions in Presence of Spatial Interference
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    Chapter 14 Continuous Geometry-Aware Graph Diffusion via Hyperbolic Neural PDE
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    Chapter 15 SpanGNN: Towards Memory-Efficient Graph Neural Networks via Spanning Subgraph Training
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    Chapter 16 AKGNet: Attribute Knowledge Guided Unsupervised Lung-Infected Area Segmentation
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    Chapter 17 Diffusion Model in Normal Gathering Latent Space for Time Series Anomaly Detection
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    Chapter 18 Permutation Dependent Feature Mixing for Multivariate Time Series Forecasting
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    Chapter 19 Prior Bilinear-Based Models for Knowledge Graph Completion
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    Chapter 20 Thinking Like an Author: A Zero-Shot Learning Approach to Keyphrase Generation with Large Language Model
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    Chapter 21 Molecular Graph Representation Learning via Structural Similarity Information
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    Chapter 22 Efficient Privacy-Preserving Truth Discovery and Copy Detection in Crowdsourcing
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    Chapter 23 FCFL: A Fairness Compensation-Based Federated Learning Scheme with Accumulated Queues
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    Chapter 24 MMDL-Based Data Augmentation with Domain Knowledge for Time Series Classification
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    Chapter 25 FALCUN: A Simple and Efficient Deep Active Learning Strategy
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    Chapter 26 Semi-supervised Heterogeneous Domain Adaptation via Disentanglement and Pseudo-labelling