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 V
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European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part V

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

Springer (Publisher)

Published on 29. August 2024

LVII, 460 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 V
Research Track
Fair Densest Subgraph Across Multiple Graphs
1 Introduction
2 Preliminary Notation and Problem Definition
3 Computational Complexity
4 Algorithms
4.1 Exact Algorithm for FDS
4.2 Exact Algorithm for SDS
4.3 Greedy Algorithm to Find a Good Solution for SDS
4.4 Greedy Algorithm to Find a Good Solution for FDS
4.5 Exact Algorithm for MDS
5 Related Work
6 Experimental Evaluation
6.1 Synthetic Dataset
6.2 Results for the Synthetic Dataset
6.3 Real-World Datasets
6.4 Results for the Real-World Datasets
6.5 Case Study
7 Concluding Remarks
References
A Human-Centric Assessment of the Usefulness of Attribution Methods in Computer Vision
1 Introduction
2 Related Work
3 Conceptual Design of the Framework
3.1 Formal Definition
3.2 Assumptions and Discussion
4 Experimental Design
4.1 Idea, Goal, and Challenges
4.2 Dataset Creation
4.3 AI Model and Attribution Methods
4.4 Implementation of the Assessment Framework
5 Results
6 Discussion
7 Conclusion
A Details of the Dataset Creation Process
B Data Compliance with GDPR
References
Leveraging Plasticity in Incremental Decision Trees
1 Introduction
2 Setting
3 Preliminaries
4 PLASTIC
4.1 Concept
4.2 Algorithm
5 Evaluation
5.1 Algorithms
5.2 Data Streams
5.3 PLASTIC vs. EFDT
5.4 Average Accuracy and Ranks
5.5 Runtime
6 Conclusion
References
Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience
1 Introduction
2 Related Work
3 Problem Formulation and Design Principles
4 Fast-FedUL
4.1 Overview
4.2 Efficient Sampling of Local Updates
4.3 Removal of Target Client Updates
4.4 Skew Estimation
4.5 Theoretical Analysis
5 Empirical Evaluation
5.1 End-to-End Comparison
5.2 Ablation Study
5.3 Qualitative Study
6 Conclusion
References
Input Compression with Positional Consistency for Efficient Training and Inference of Transformer Neural Networks
1 Introduction
2 Data Augmentation Through Input Compression
3 Consistency-Aware Position Selection: Enabling ICPC in an Architecture-Agnostic Manner
4 Efficient Variable-Effort Inference Using ICPC
5 Experimental Results
5.1 Primary Results
5.2 Ablation: Evaluation of ICPC as a Data Augmentation Method
5.3 Improving Accuracy with Hardware-Aware Test-Time Augmentation
6 Related Work
7 Conclusion
References
Individual Fairness with Group Awareness Under Uncertainty
1 Introduction
2 Related Work
2.1 Fairness in ML
2.2 Survival Analysis
3 Notations and Problem Definition
4 Methodology
4.1 Quantifying Individual Fairness with Censorship
4.2 Quantifying Individual-Group Fairness with Censorship
4.3 Mitigating Bias Under Censorship
5 Experiment
5.1 Datasets
5.2 Baselines
5.3 Evaluation Metrics
5.4 Experimental Results
6 Conclusion
References
Generalizing Reward Modeling for Out-of-Distribution Preference Learning
1 Introduction
2 Preliminaries
3 Meta-learning for Generalizing Reward Modeling
3.1 Preference Learning via Bilevel Programming
3.2 Meta-learning for Out-of-Distribution Preference Learning
3.3 Gradient-Based Algorithm
3.4 Convergence Analysis
4 Experiments
4.1 Experimental Setup
4.2 Evaluation with Preference Data
4.3 Evaluation with a Learned RM
4.4 Evaluation with GPT-4 Judgement
4.5 Effects of on Reward Learning
5 Conclusion
References
FedPrime: An Adaptive Critical Learning Periods Control Framework for Efficient Federated Learning in Heterogeneity Scenarios
1 Introduction
2 Related Work
2.1 Client Selection in Federated Learning
2.2 Critical Learning Periods in Federated Learning
3 Problem Formulation
4 The Design of FedPrime Framework
4.1 The Overview of FedPrime Framework
4.2 Client Selection Module
4.3 Adaptive CLP Control Module
5 Experiment
5.1 Experimental Setup
5.2 Test Accuracy
5.3 Generalization
5.4 Ablation Study
6 Conclusion
References
What Drives Online Popularity: Author, Content or Sharers? Estimating Spread Dynamics with Bayesian Mixture Hawkes
1 Introduction
2 Preliminaries
2.1 Hawkes Process
2.2 Dual Mixture Model
3 Bayesian Mixture Hawkes (BMH) Model
3.1 BMH-P, the Popularity Submodel
3.2 BMH-K, the Kernel Submodel
4 Predictive Evaluation
4.1 Datasets
4.2 Cold-Start Popularity Prediction
4.3 Temporal Profile Generalization Performance
5 What-If? Headline Style Profiling
6 Conclusion and Future Work
References
Disagreement Evaluation of Solutions for Math Word Problem
1 Introduction
2 Related Work
3 Method
3.1 Problem Statement
3.2 LoRA Fine-Tuning
3.3 Proposed Method
4 Theoretical Analysis
5 Experiment
5.1 Experimental Setup
5.2 Results and Analysis
5.3 Case Study
6 Discussion
7 Conclusion
References
Co-attention and Contrastive Learning Driven Knowledge Tracing
1 Introduction
2 Related Work
2.1 Knowledge Tracing
2.2 Contrastive Learning
3 Preliminary
4 Methodology
4.1 Learning Activity Representation
4.2 Ability and Knowledge Co-attention Layer
4.3 Knowledge Internalization Encoder
4.4 Contrastive Learning
4.5 Performance Prediction
5 Experiment
5.1 Datasets
5.2 Baseline Methods
5.3 Experimental Settings
5.4 Performance Prediction Results
5.5 Ablation Study
5.6 Parameter Sensitivity Analysis
5.7 Visualizations
6 Conclusion
References
TiNID: A Transfer and Interpretable LLM-Enhanced Framework for New Intent Discovery
1 Introduction
2 TiNID
2.1 Problem Formalization
2.2 Model Overview
2.3 Knowledge Generation
2.4 Knowledge Decoupling
2.5 Knowledge Transfer
2.6 Knowledge Interpretation
3 Experiments
3.1 Datasets and Baseline Models
3.2 Evaluation Metrics
3.3 Implementation Details
3.4 Main Results
4 Qualitative Analysis
5 Related Work
6 Conclusion
References
Memory-Enhanced Emotional Support Conversations with Motivation-Driven Strategy Inference
1 Introduction
2 Related Work
2.1 Emotional Support Conversation Systems
2.2 Empathetic Dialogue Systems
3 Methodology
3.1 Task Definition and Model Overview
3.2 Context Encoder
3.3 Motivation-Driven Support Strategy Inference
3.4 Memory-Enhanced Strategy Modeling
3.5 Response Decoder
3.6 Model Optimization
4 Experiments
4.1 Datasets, Baselines and Evaluation Metrics
4.2 Implementation Details
4.3 Experimental Results
4.4 Discussions
4.5 Case Study
5 Conclusion
References
Semi-automated Construction of Complex Knowledge Base Question Answering Dataset Using Large Language Model
1 Introduction
2 Related Work
3 Enhancing the Movie Knowledge Graph (iMKG)
3.1 Assigning Wikidata URI to Movie Entities of MovieKG
3.2 Assigning Wikidata URI to Other Entities of MovieKG
4 Leveraging LLM to Construct the Movie Complex Question Answering (MCQA) Dataset
4.1 Question Types Creation
4.2 Templates Creation
4.3 Data Sampling
5 Experiments
5.1 Experimental Setup
5.2 Evaluating KBQA Methods on MCQA
6 Conclusion
References
Graphical Model-Based Lasso for Weakly Dependent Time Series of Tensors
1 Introduction
2 Methodology
2.1 Notation
2.2 Model Formulation
2.3 Near-Oracle Estimation Error Bound
3 Simulation Study
3.1 Experimental Setting
3.2 Results
4 Crypto-Asset Portfolio Construction
5 Conclusion
References
Multi-label Adaptive Batch Selection by Highlighting Hard and Imbalanced Samples
1 Introduction
2 Related Work
2.1 Multi-label Classification
2.2 Hard Sample and Batch Selection in Single Label Data
3 Proposed Method
3.1 Preliminaries
3.2 Rank-Based Batch Selection
3.3 Class Imbalance Aware Weighting
3.4 Incorporate Quantization Index
3.5 Variant of Adaptive Batch Selection Exploiting Label Correlations
3.6 Convergence Guarantee
4 Experiments and Analysis
4.1 Experiment Setup
4.2 Experimental Results
4.3 Convergence Analysis
4.4 Adaptive Batch Selection with Label Correlations
4.5 Investigation of Loss
4.6 Parameter Analysis
5 Conclusion
References
Quantification Over Time
1 Introduction
2 Definitions, Notation and Background
2.1 Quantification
2.2 Quantification over Time (QoT)
2.3 QoT, Quantification and Time Series Forecasting
3 Related Work
3.1 QoT with Quantification only
3.2 QoT with Classify and Count and Moving Average
4 Methodology
4.1 Adjustment Framework
4.2 KF-MA
5 Experimental Evaluation
5.1 Experimental Setup
5.2 Comparison with QoT Approaches in Literature
5.3 Results
6 Conclusion
References
Approximating the Graph Edit Distance with Compact Neighborhood Representations
1 Introduction
2 Preliminaries
3 Related Work
4 Neighborhood Trees for Graph Matching
4.1 Definitions and Properties
4.2 Creating Compressed Neighborhood Trees
4.3 Neighborhood Tree Edit Distance
4.4 Deriving an Edit Path
4.5 Theoretical Complexity
4.6 Optimization Strategies
5 Experimental Evaluation
6 Conclusions
References
Online L-Convex Minimization
1 Introduction
1.1 Contributions
1.2 Related Work
2 Preliminaries and Problem Statement
2.1 Online L-Convex Minimization
2.2 Lovász Extension of Submodular Functions
2.3 Convex Extension of L-Convex Functions
2.4 Subgradient of the Convex Extension of L-Convex Function
3 Upper Bound on Regret
3.1 Full Information Setting
3.2 Bandit Setting
4 Lower Bound on Regret
5 Applications
5.1 Online Inventory System of Reparable Spare Parts
5.2 Shift Scheduling with a Global Service Level Constraint
6 Conclusions
References
Preserving Real-World Robustness of Neural Networks Under Sparsity Constraints
1 Introduction
2 Related Work
2.1 Robustness of Deep Neural Networks in Real-World
2.2 Model Compression via Pruning
2.3 Preserving Robustness in Sparse Neural Networks
3 Evaluating Real-World Robustness in Neural Networks
3.1 Experimental Setup
3.2 Preliminary Evaluations with Dense Models
4 Preserving Robustness During Model Compression
4.1 Proposed Approach
4.2 Results over Different Sparsity Levels
4.3 Further Experiments
5 Discussion
References
LTCR: Long Temporal Characteristic Reconstruction for Segmentation in Contrastive Learning
1 Introduction
2 Related Work
3 Methods
3.1 Problem Formulation
3.2 Periodic Characteristics Reconstruction
3.3 Trend Characteristics Reconstruction
3.4 Overall Architecture
4 Experiments
4.1 Datasets
4.2 Experimental Setup
4.3 Experimental Results
4.4 Visualization
4.5 Ablation Study
4.6 More Epochs for Baselines
5 Conclusion
References
LATuner: An LLM-Enhanced Database Tuning System Based on Adaptive Surrogate Model
1 Introduction
2 Background
3 Workflow of LATuner
4 Methodology
4.1 Konb Selector
4.2 Zero-Shot Warm-Start Strategy
4.3 Hybrid Candidate Configuration Sampler
4.4 Adaptive Surrogate Model
5 Experiments
5.1 Experimental Setup
5.2 Experimental Results Analysis
6 Conclusion
References
Improving Meta-learning for Few-Shot Text Classification via Label Propagation
1 Introduction
2 Related Work
2.1 Meta-learning
2.2 Few-Shot Text Classification
3 Our Proposed Method
3.1 Problem Formulation
3.2 Basic Few-Shot Classifier
3.3 Label Propagation for Prototypical Network
3.4 Attention-Based Distance Metric
3.5 Meta-training Objectives
4 Experiments
4.1 Datasets
4.2 Baselines
4.3 Implementation Details
4.4 Result Analysis
4.5 Ablation Study
4.6 Visualization
5 Conclusion
References
Zero-cost Transition to Multi-document Processing in Summarization with Multi-Channel Attention
1 Introduction
2 Related Work
2.1 Horizontal Scaling
2.2 Parallel Encoding
3 Proposed Methods
3.1 Factorizing Estimation
3.2 Multi-Channel Attention Architecture
4 Experiments
4.1 Zero-cost Transition to Multi-doc Summarization
4.2 Optimizing MCA
5 Conclusion
References
PSP: Pre-training and Structure Prompt Tuning for Graph Neural Networks
1 Introduction
2 Related Work
2.1 Graph Pre-training
2.2 Prompt-Based Learning
3 Preliminary
4 Proposed Method
4.1 Graph Pre-training
4.2 Graph Structure Prompt Tuning
4.3 Inference
5 Experiments
5.1 Experimental Setup
5.2 Node Classification
5.3 Few-Shot Node Classification
5.4 Few-Shot Graph Classification
5.5 Model Analysis
6 Conclusion
References
Wiser than the Wisest of Crowds: The Asch Effect and Polarization Revisited
1 Introduction
2 Related Work
3 Preliminaries
4 Problem Definitions
5 Hardness
6 Proposed Methods
7 Experimental Evaluation
7.1 Setup
7.2 Real-World Experiments
8 Conclusion
References
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Machine Learning and Knowledge Discovery in Databases. Research Track

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