Machine Learning and Knowledge Discovery in Databases. Research Track

Name: Machine Learning and Knowledge Discovery in Databases. Research Track | European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part VI
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European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part VI

Albert Bifet Jesse Davis Tomas Krilavicius Meelis Kull Eirini Ntoutsi Indre Zliobaite(Editor)

Springer (Publisher)

Published on 1. September 2024

LVII, 458 pages

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This multi-volume set, LNAI 14941 to LNAI 14950, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2024, held in Vilnius, Lithuania, in September 2024.

The papers presented in these proceedings are from the following three conference tracks: -

Research Track: The 202 full papers presented here, from this track, were carefully reviewed and selected from 826 submissions. These papers are present in the following volumes: Part I, II, III, IV, V, VI, VII, VIII.

Demo Track: The 14 papers presented here, from this track, were selected from 30 submissions. These papers are present in the following volume: Part VIII.

Applied Data Science Track: The 56 full papers presented here, from this track, were carefully reviewed and selected from 224 submissions. These papers are present in the following volumes: Part IX and Part X.

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Content

Intro
Preface
Organization
Invited Talks Abstracts
The Dynamics of Memorization and Unlearning
The Emerging Science of Benchmarks
Enhancing User Experience with AI-Powered Search and Recommendations at Spotify
How to Utilize (and Generate) Player Tracking Data in Sport
Resource-Aware Machine Learning-A User-Oriented Approach
Contents - Part VI
Research Track
Rejection Ensembles with Online Calibration
1 Introduction
2 Notation and Related Work
2.1 Related Work
3 A Theoretical Investigation of Rejection
3.1 Three Distinct Situations Can Occur When Training the Rejector
3.2 Even a Perfect Rejector Will Overuse Its Budget
3.3 A Rejector Should Not Trust fs and fb
4 Training a Rejector for a Rejection Ensemble
5 Experiments
5.1 Experiments with Deep Learning Models
5.2 Experiments with Decision Trees
5.3 Conclusion from the Experiments
6 Conclusion
References
Lighter, Better, Faster Multi-source Domain Adaptation with Gaussian Mixture Models and Optimal Transport
1 Introduction
2 Preliminaries
2.1 Gaussian Mixtures
2.2 Domain Adaptation
2.3 Optimal Transport
3 Methodological Contributions
3.1 First Order Analysis of MW2
3.2 Supervised Mixture-Wasserstein Distances
3.3 Mixture Wasserstein Barycenters
3.4 Multi-source Domain Adaptation Through GMM-OT
4 Experiments
4.1 Toy Example
4.2 Multi-source Domain Adaptation
4.3 Lighter, Better, Faster Domain Adaptation
5 Conclusion
References
Subgraph Retrieval Enhanced by Graph-Text Alignment for Commonsense Question Answering
1 Introduction
2 Related Work
2.1 Commonsense Question Answering
2.2 Graph-Text Alignment
3 Task Formulation
4 Methods
4.1 Graph-Text Alignment
4.2 Subgraph Retrieval Module
4.3 Prediction
5 Experiments
5.1 Datasets
5.2 Baselines
5.3 Implementation Details
5.4 Main Results
5.5 Ablation Study
5.6 Low-Resource Setting
5.7 Evaluation with other GNNs
5.8 Hyper-parameter Analysis
6 Ethical Considerations and Limitations
6.1 Ethical Considerations
6.2 Limitations
7 Conclusion
References
HetCAN: A Heterogeneous Graph Cascade Attention Network with Dual-Level Awareness
1 Introduction
2 Related Work
3 Preliminaries
3.1 Heterogeneous Information Network
3.2 Graph Neural Networks
3.3 Transformer-Style Architecture
4 The Proposed Model
4.1 Overall Architecture
4.2 Type-Aware Encoder
4.3 Dimension-Aware Encoder
4.4 Time Complexity Analysis
5 Experiments
5.1 Experimental Setups
5.2 Node Classification
5.3 Link Prediction
5.4 Model Analysis
6 Conclusion
References
Interpetable Target-Feature Aggregation for Multi-task Learning Based on Bias-Variance Analysis
1 Introduction
2 Preliminaries
2.1 Related Works: Dimensionality Reduction, Multi-task Learning
3 Bias-Variance Analysis: Theoretical Results
4 Multi-task Learning via Aggregations: Algorithms
5 Experimental Validation
5.1 Synthetic Experiments and Ablation Study
5.2 Real World Datasets
6 Conclusions and Future Developments
References
The Simpler The Better: An Entropy-Based Importance Metric to Reduce Neural Networks' Depth
1 Introduction
2 Related Works
3 Method
3.1 How Layers Can Degenerate
3.2 Entropy for Rectifier Activations
3.3 EASIER
4 Experiments
4.1 Experimental Setup
4.2 Results
4.3 Ablation Study
4.4 Limitations and Future Work
5 Conclusion
References
Towards Few-Shot Self-explaining Graph Neural Networks
1 Introduction
2 Problem Definition
3 The Proposed MSE-GNN
3.1 Architecture of MSE-GNN
3.2 Optimization Objective
3.3 Meta Training
4 Experiments
4.1 Datasets and Experimental Setup
5 Related Works
6 Conclusion
References
Uplift Modeling Under Limited Supervision
1 Introduction
2 Related Work
3 Proposed Methodology
3.1 Uplift Modeling with Graph Neural Networks (UMGNet)
3.2 Active Learning for Uplift GNNs (UMGNet-AL)
4 Experimental Evaluation
4.1 Datasets
4.2 Benchmark Models
4.3 Experiments
5 Conclusion
References
Self-supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection
1 Introduction
2 Related Work
3 STEN: Spatial-Temporal Normality Learning
3.1 Problem Statement
3.2 Overview of The Proposed Approach
3.3 OTN: Order Prediction-Based Temporal Normality Learning
3.4 DSN: Distance Prediction-Based Spatial Normality Learning
3.5 Training and Inference
4 Experiments
4.1 Experimental Setup
4.2 Main Results
4.3 Ablation Study
4.4 Qualitative Analysis
4.5 Sensitivity Analysis
4.6 Time Efficiency
5 Conclusion
References
Modeling Text-Label Alignment for Hierarchical Text Classification
1 Introduction
2 Related Work
3 Methodology
3.1 Text Encoder
3.2 Graph Encoder
3.3 Generation of Composite Representation
3.4 Loss Functions
4 Experiments
4.1 Datasets and Evaluation Metrics
4.2 Implementation Details
4.3 Experimental Results
4.4 Analysis
5 Conclusion
A Details of Statistical Test
B Performance Analysis on Additional Datasets
References
Secure Aggregation Is Not Private Against Membership Inference Attacks
1 Introduction
2 Related Work
3 Preliminaries
4 Privacy Analysis of Secure Aggregation
4.1 Threat Model
4.2 SecAgg as a Noiseless LDP Mechanism
4.3 Asymptotic Privacy Guarantee
4.4 Upper Bounding M() via Dominating Pairs of Distributions
4.5 Lower Bounding M() and Upper Bounding fM() via Privacy Auditing
5 Experiments and Discussion
6 Conclusions
A Correlated Gaussian Mechanism
A.1 Optimal LDP Curve: Proof of Theorem 2
A.2 The Case Sd={xRd:||x||2 rd}
A.3 Trade-Off Function: Proof of Proposition 1
B LDP Analysis of the Mechanism (1) in a Special Case: Proof of Theorem 3
References
Evaluating Negation with Multi-way Joins Accelerates Class Expression Learning
1 Introduction
2 Preliminaries
2.1 The Description Logic ALC
2.2 Class Expression Learning
2.3 Semantics and Properties of SPARQL
2.4 Worst-Case Optimal Multi-way Join Algorithms
3 Mapping ALC Class Expressions to SPARQL Queries
4 Negation in Multi-way Joins
4.1 Rewriting Rule for Negation and UNION Normal Form
4.2 Multi-way Join Algorithm
4.3 Implementation
5 Experimental Results
5.1 Systems, Setup and Execution
5.2 Datasets and Queries
5.3 Results and Discussion
6 Related Work
7 Conclusion And Future Work
References
LayeredLiNGAM: A Practical and Fast Method for Learning a Linear Non-gaussian Structural Equation Model
1 Introduction
2 Related Work
3 Preliminaries
3.1 LiNGAM
3.2 DirectLiNGAM
4 LayeredLiNGAM
4.1 Generalization of Lemma 2
4.2 Algorithm
4.3 Adaptive Thresholding
5 Experiments
5.1 Datasets and Evaluation Metrics
5.2 Determining Threshold Parameters
5.3 Results on Synthetic Datasets
5.4 Results on Real-World Datasets
6 Conclusion
References
Enhanced Bayesian Optimization via Preferential Modeling of Abstract Properties
1 Introduction
2 Background
2.1 Bayesian Optimization
2.2 Rank GP Distributions
3 Framework
3.1 Expert Preferential Inputs on Abstract Properties
3.2 Augmented GP with Abstract Property Preferences
3.3 Overcoming Inaccurate Expert Inputs
4 Convergence Remarks
5 Experiments
5.1 Synthetic Experiments
5.2 Real-World Experiments
6 Conclusion
References
Enhancing LLM's Reliability by Iterative Verification Attributions with Keyword Fronting
1 Introduction
2 Related Work
2.1 Retrieval-Augmented Generation
2.2 Text Generation Attribution
3 Methodology
3.1 Task Formalization
3.2 Overall Framework
3.3 Keyword Fronting
3.4 Attribution Verification
3.5 Iterative Optimization
4 Experiments
4.1 Experimental Setup
4.2 Main Results
4.3 Ablation Studies
4.4 Impact of Hyperparameters
4.5 The Performance of the Iteration
5 Conclusion
References
Reconstructing the Unseen: GRIOT for Attributed Graph Imputation with Optimal Transport
1 Introduction
2 Related Works
3 Multi-view Optimal Transport Loss for Attribute Imputation
3.1 Notations
3.2 Optimal Transport and Wasserstein Distance
3.3 Definition of the `3´9`42`"?613A``45`47`"603AMultiW Loss Function
3.4 Instantiation of `3´9`42`"?613A``45`47`"603AMultiW Loss with Attributes and Structure
4 Imputing Missing Attributes with `3´9`42`"?613A``45`47`"603AMultiW Loss
4.1 Architecture of GRIOT
4.2 Accelerating the Imputation
5 Experimental Analysis
5.1 Experimental Protocol
5.2 Imputation Quality v.s. Node Classification Accuracy
5.3 Imputing Missing Values for Unseen Nodes
5.4 Time Complexity
6 Conclusion and Perspectives
References
Introducing Total Harmonic Resistance for Graph Robustness Under Edge Deletions
1 Introduction
2 Problem Statement and a New Robustness Measure
2.1 Problem Statement and Notation
2.2 Robustness Measures
3 Related Work
4 Comparison of Exact Solutions
5 Greedy Heuristic for k-GRoDel
5.1 Total Harmonic Resistance Loss After Deleting an Edge
5.2 Forest Index Loss After Deleting an Edge
6 Experimental Results
6.1 Experimental Setup
6.2 Case Study: Berlin Districts
6.3 Benchmark Results
7 Conclusions
References
Counterfactual-Based Root Cause Analysis for Dynamical Systems
1 Introduction
2 Related Work
3 Background and Notation
3.1 Root Cause
3.2 Shapley Value
4 Method for Identifying Root Causes
5 Experiments
5.1 Experimental Datasets
5.2 Evaluation
6 Conclusion
References
Dropout Regularization in Extended Generalized Linear Models Based on Double Exponential Families
1 Introduction
2 Generalized Linear Models Based on Double Exponential Families
2.1 Double Exponential Family
2.2 GLMs Based on DEFs
3 Dropout Regularization in GLMs Based on DEFs
3.1 Dropout Regularization
3.2 Dropout Regularization for the Mean Parameter
3.3 Dropout Regularization for the Mean and Dispersion Parameter
4 Application to Adaptive Smoothing with B-Splines
4.1 Simulations
4.2 Traffic Detection Data
5 Conclusion
References
GLADformer: A Mixed Perspective for Graph-Level Anomaly Detection
1 Introduction
2 Related Work
2.1 Graph-Level Anomaly Detection
2.2 Graph Transformer
3 Preliminary
3.1 Problem Definition
3.2 Graph Spectrum and Rayleigh Quotient
4 Method
4.1 Spectrum-Enhanced Graph Transformer Module
4.2 Local Spectral Message-Passing Module
4.3 Variation-Optimize Cross-Entropy Loss Function
5 Experiment
5.1 Experiment Settings
5.2 Main Results
5.3 Ablation Study
5.4 Visualization Analysis
6 Conclusion
References
HiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target Interaction Prediction
1 Introduction
2 Related Work
3 Method
3.1 Hierarchical Molecular Graph Representation
3.2 Attentional Target Feature Fusion
3.3 Hierarchical Attention Mechanism
4 Experiments
4.1 Experimental Setup
4.2 Comparison Results
4.3 Ablation Experiment
4.4 Attention Interpretation
5 Conclusion
References
On the Two Sides of Redundancy in Graph Neural Networks
1 Introduction
2 Related Work
3 Preliminaries
4 Non-redundant Graph Neural Networks
4.1 Removing Information Redundancy
4.2 Removing Computational Redundancy
4.3 Non-redundant Neural Architecture (DAG-MLP)
4.4 Expressivity of k-NTs
4.5 Theoretical Analysis and Comparison to Related Work
5 Experimental Evaluation
6 Conclusion
References
Policy Control with Delayed, Aggregate, and Anonymous Feedback
1 Introduction
2 Related Work
3 Problem Formulation
3.1 Preliminaries
3.2 Policy Control
3.3 Delayed, Aggregate, Anonymous Feedback
4 Algorithms Development
5 Experiments
5.1 Returns
5.2 Sample Efficacy
5.3 Stochastic Similarity to Full Rewards Policies
6 Discussion
7 Conclusion
References
DiffVersify: a Scalable Approach to Differentiable Pattern Mining with Coverage Regularization
1 Introduction
2 Related Work
3 Preliminaries
4 Differentiable Pattern Mining with Coverage Regularization
4.1 Neural Model for Pattern Mining
4.2 Learning Algorithm
4.3 Pattern Decoding from Latent Representation
5 Experiments
5.1 Metrics
5.2 Baselines
5.3 Experiments on Real-World Benchmarks
5.4 Experiments on Synthetic Data
6 Conclusion
References
Hierarchical Graph Contrastive Learning for Review-Enhanced Recommendation
1 Introduction
2 Related Work
2.1 GNNs for Review-Enhanced Recommendation
2.2 Contrastive Learning
3 Problem Definition
4 Methodology
4.1 Subgraph Partition and Review-Aware Graph Convolution
4.2 Multi-rating Contrastive Learning
4.3 Hypergraph Structure Learning
4.4 Global-Local Contrastive Learning
4.5 Edge-Level Contrastive Learning
4.6 Model Optimization
5 Experiment
5.1 Experimental Settings
5.2 Comparison and Result Analysis
5.3 Ablation Study
5.4 Impact of Imbalanced Data Distribution
5.5 Parameter Sensitivity
6 Conclusion
References
Linear Contextual Bandits with Hybrid Payoff: Revisited
1 Introduction
1.1 Our Contributions
1.2 Additional Remarks on Contributions
1.3 Related Work
2 Problem Formulation
3 Algorithms and Analysis
3.1 LinUCB and DisLinUCB
3.2 HyLinUCB
4 Experimental Setup
4.1 Synthetic
4.2 Real-World
5 Results
5.1 Synthetic Experiments
5.2 Real-World Experiment
6 Conclusion and Future Work
References
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Machine Learning and Knowledge Discovery in Databases. Research Track

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