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 I
Brand: Springer
Price: 85.59 EUR
Availability: OnlineOnly

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

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

Springer (Publisher)

Published on 30. August 2024

LVII, 462 pages

E-Book

PDF with digital watermarking

System requirements

978-3-031-70341-6 (ISBN)

€85.59incl. 7% vat

System requirements

for PDF with digital watermarking

E-Book Single Licence

Available for download

Description

Alles über E-Books | Antworten auf Fragen rund um E-Books, Kopierschutz und Dateiformate finden Sie in unserem Info- & Hilfebereich.

Alles über E-Books, Kopierschutz & Dateiformate finden Sie in unserem Info- & Hilfebereich.

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.

More details

Other editions

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 I
Research Track
Adaptive Sparsity Level During Training for Efficient Time Series Forecasting with Transformers
1 Introduction
2 Background
2.1 Sparse Neural Networks
2.2 Time Series Forecasting
2.3 Problem Formulation and Notations
3 Analyzing Sparsity Effect in Transformers for Time Series Forecasting
4 Proposed Methodology: PALS
5 Experiments and Results
5.1 Experimental Settings
5.2 Results
6 Discussion
6.1 Performance Comparison with Pruning and Sparse Training Algorithms
6.2 Hyperparameter Sensitivity
7 Conclusions
References
RumorMixer: Exploring Echo Chamber Effect and Platform Heterogeneity for Rumor Detection
1 Introduction
2 Related Works
3 Methodology
3.1 Overview
3.2 Echo Chamber Extraction and Representation Learning
3.3 Neural Architecture Search for Platform Heterogeneity
4 Experiments
4.1 Experimental Setting
4.2 Performance Comparison (RQ1)
4.3 Ablation Study (RQ2)
4.4 Parameter Analysis (RQ3)
4.5 Early Rumor Detection (RQ4)
5 Conclusion
References
Diversified Ensemble of Independent Sub-networks for Robust Self-supervised Representation Learning
1 Introduction
2 Related Work
3 Method
3.1 Robust Self-supervised Learning via Independent Sub-networks
3.2 Empirical Analysis of Diversity
3.3 Computational Cost and Efficiency Analysis
4 Experimental Setup
5 Results and Discussion
6 Ablation Study
7 Conclusion
References
Modular Debiasing of Latent User Representations in Prototype-Based Recommender Systems
1 Introduction
2 Related Work
3 Methodology
4 Experiment Setup
5 Results and Analysis
6 Conclusion and Future Directions
References
A Mathematics Framework of Artificial Shifted Population Risk and Its Further Understanding Related to Consistency Regularization
1 Introduction
2 Related Work
3 Method
3.1 Revisiting Data Augmentation with Empirical Risk
3.2 The Augmented Neighborhood
3.3 The Artificial Shifted Population Risk
3.4 Understanding the Decomposition of Shifted Population Risk
4 Experiment
4.1 Experiment Implementation
4.2 Experimental Results
5 Conclusion and Discussion
References
Attention-Driven Dropout: A Simple Method to Improve Self-supervised Contrastive Sentence Embeddings
1 Introduction
2 Background and Related Work
3 Method
3.1 Attention Rollout Aggregation
3.2 Static Dropout Rate
3.3 Dynamic Dropout Rate
4 Experiment
4.1 Datasets and Tasks
4.2 Training Procedure
5 Result and Discussion
5.1 Ablation Study
6 Conclusion
References
AEMLO: AutoEncoder-Guided Multi-label Oversampling
1 Introduction
1.1 Research Goal
1.2 Motivation
1.3 Summary
2 Related Work
2.1 Multi-label Classification
2.2 Multi-label Imbalance Learning
2.3 Deep Sampling Method
3 Multi-label AutoEncoder Oversampling
3.1 Method Description and Overview
3.2 Loss Function
3.3 Generate Instances and Post-processing
4 Experiments and Analysis
4.1 Datasets
4.2 Experiment Setup
4.3 Experimental Analysis
4.4 Parameter Analysis
4.5 Sampling Time
5 Conclusion
References
MANTRA: Temporal Betweenness Centrality Approximation Through Sampling
1 Introduction
2 Related Work
3 Preliminaries
4 MANTRA: Temporal Betweenness Centrality Approxi-mation Through Sampling
4.1 Temporal Betweenness Estimator
4.2 Sample Complexity Bounds
4.3 Fast Approximation of the Characteristic Quantities
4.4 The MANTRA Framework
5 Experimental Evaluation
5.1 Experimental Setting
5.2 Networks
5.3 Experimental Results
6 Conclusions
References
Dimensionality-Induced Information Loss of Outliers in Deep Neural Networks
1 Introduction
2 Problem Setting and Related Work
2.1 Stable Rank of the Matrix
2.2 Feature-Based Detection
2.3 Projection-Based Detection
2.4 Similarity of DNN Representations
2.5 Noise Sensitivity in the DNN
3 Results
3.1 Overview of the Experiments and a Possible Picture
3.2 Observation of Dimensionality via Stable Ranks
3.3 Transition of OOD Detection Performance
3.4 Block Structure of CKA
3.5 Instability of OOD Samples to Noise Injection
3.6 Dataset Bias-Induced Imbalanced Inference
3.7 Quantitative Comparison of OOD Detection Performance
4 Discussion
5 Summary and Conclusion
References
Towards Open-World Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach
1 Introduction
2 Methodology
2.1 Problem Formulation
2.2 Embedding Encoder
2.3 Denoising Interest-Aware Network
2.4 Fusion Gate Unit
2.5 Model Training
2.6 Inductive Representation Generator
3 Experiments
3.1 Datasets
3.2 Experiment Setting
3.3 Performance Comparisons (RQ1)
3.4 Ablation Study (RQ2)
3.5 Online Evaluation (RQ3)
3.6 Model Analyses (RQ4)
3.7 Parameter Sensitivity (RQ5)
4 Related Work
5 Conclusion
References
MixerFlow: MLP-Mixer Meets Normalising Flows
1 Introduction
2 Related Works
3 Preleminaries
4 MixerFlow Architecture and Its Components
5 Experiments
5.1 Density Estimation on 3232 Datasets
5.2 Density Estimation on 6464 Datasets
5.3 Enhancing MAF with the MixerFlow
5.4 Datasets with Specific Permutations
5.5 Hybrid Modelling
5.6 Integration of Powerful Architecture
6 Conclusion and Limitations
7 Future Work and Broader Impact
References
Handling Delayed Feedback in Distributed Online Optimization: A Projection-Free Approach
1 Introduction
1.1 Our Contribution
1.2 Related Work
2 Projection-Free Algorithms Under Delayed Feedback
2.1 Preliminaries
2.2 Centralized Algorithm
2.3 Distributed Algorithm
3 Numerical Experiments
4 Concluding Remarks
References
Secure Dataset Condensation for Privacy-Preserving and Efficient Vertical Federated Learning
1 Introduction
2 Related Work
2.1 Vertical Federated Learning
2.2 Privacy Protection in VFL
2.3 Dataset Size Reduction in FL
3 Preliminaries
3.1 Problem Formulation
3.2 Dataset Condensation
3.3 Secure Aggregation
3.4 Differential Privacy
4 Proposed Approach
4.1 Overview
4.2 Class-Wise Secure Aggregation
4.3 VFDC Algorithm
4.4 Privacy Analysis
5 Experimental Study
5.1 Experimental Setup
5.2 Visualization of Condensed Dataset
5.3 Performance Comparison
5.4 Efficiency Improvement
5.5 Impact of Hyperparameters
6 Conclusion and Future Directions
References
Neighborhood Component Feature Selection for Multiple Instance Learning Paradigm
1 Introduction
2 Methods
2.1 The Lazy Learning Approach for Multiple Instance Learning Setting
2.2 Neighborhood Component Feature Selection for Single Instance Learning Setting
2.3 Our Proposal: Neighborhood Component Feature Selection for the Multiple Instance Learning Setting
3 Datasets
3.1 Musk Dataset
3.2 DEAP Dataset
4 Experimental Procedure
5 Experimental Results
5.1 Musk Dataset
5.2 DEAP Dataset
5.3 Comparison with State-of-the-Art
5.4 Statistical Significance
5.5 Computational Complexity
6 Conclusions
References
MESS: Coarse-Grained Modular Two-Way Dialogue Entity Linking Framework
1 Introduction
2 Related Work
2.1 Mention-to-Entities
2.2 Transferred EL
3 Our MESS Framework
3.1 M2E Module
3.2 E2M Module
3.3 SS Module
3.4 Dialogue Module
4 Experiments
4.1 Setting
4.2 Results
4.3 Ablation Studies
5 Conclusion
References
Session Target Pair: User Intent Perceiving Networks for Session-Based Recommendation
1 Introduction
2 Related Work
3 Methodology
3.1 Problem Statement
3.2 Session-Level Intent Representation Module
3.3 Target-Level Intent Representation Module
3.4 Intent Alignment Mechanism Module
3.5 Prediction and Training
4 Experiments
4.1 Experiment Setups
4.2 Overall Performance
4.3 Model Analysis and Discussion
5 Conclusion
References
Hierarchical Fine-Grained Visual Classification Leveraging Consistent Hierarchical Knowledge
1 Introduction
2 Related Work
2.1 Fine-Grained Visual Classification
2.2 Hierarchical Multi-granularity Classification
2.3 Graph Representation Learning
3 Approach
3.1 Problem Setting
3.2 Multi-granularity Graph Convolutional Neural Network
3.3 Hierarchy-Aware Conditional Supervised Learning
3.4 Loss Function
3.5 Tree-Structured Granularity Consistency Rate
4 Experiments
4.1 Datasets
4.2 Experimental Settings
4.3 Compared Methods
4.4 Ablation Study
4.5 Comparison with State-of-the-Art Method
4.6 Qualitative Analysis
5 Conclusion
References
Backdoor Attacks with Input-Unique Triggers in NLP
1 Introduction
2 Related Work
3 Method
3.1 Problem Formulation
3.2 NURA: Input-Unique Backdoor Attack
3.3 Model Training
4 Experiments
4.1 Experiments Setup
4.2 Implementation Details
4.3 Results for Backdoor Attacks
4.4 Results for Defenses
4.5 Trigger Quality Analysis
4.6 Case Study
4.7 User Study
5 Conclusion and Future Work
6 Limitations
References
Label Privacy Source Coding in Vertical Federated Learning
1 Introduction
2 Related Work
3 Problem Formulation
3.1 Vertical Federated Learning Setting
3.2 Threat Model
4 Proposed Label Privacy Source Coding
4.1 Preliminary
4.2 Label Privacy Source Coding Problem
4.3 Privacy Analysis
4.4 Gradient Boosting Solves LPSC Problem
5 LPSC+ Framework
5.1 Framework Architecture
5.2 Learning Objectives
6 Experiments
6.1 Experimental Setting
6.2 LPSC Protects Privacy Barely Compromising Utility
6.3 Privacy-Utility Trade-Off Comparison
6.4 Impact of Gradient Boosting Algorithms on LPSC
7 Conclusion
References
Error Types in Transformer-Based Paraphrasing Models: A Taxonomy, Paraphrase Annotation Model and Dataset
1 Introduction
2 Related Work
3 Paraphrase Generation
3.1 Selection of Seed Utterances
3.2 Selection of Models
3.3 Generation of Paraphrases from Utterances
4 Paraphrasing Error Types
4.1 Language Errors
4.2 Slot Errors
4.3 Errors of Human Characteristics
5 Creation of Annotated Paraphrasing Error Dataset
5.1 The TPME Dataset
5.2 Insights into Error Frequency and Co-occurrences
5.3 Analysis of the Annotated Paraphrases
6 BERT-Based Multi-label Paraphrase Annotation Model
6.1 BERT Fine-Tuning
6.2 Evaluation
7 Conclusion and Future Work
References
FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling
1 Introduction
2 Methodology
2.1 Problem Formulation
2.2 Overview of FedHCDR
2.3 High/Low-Pass Hypergraph Filter
2.4 Local-Global Bi-directional Transfer Algorithm
2.5 Hypergraph Contrastive Loss
3 Experiments
3.1 Experimental Setup
3.2 Performance Comparisons (RQ1)
3.3 Ablation Study (RQ2)
3.4 Discussion of the User Representation (RQ3)
3.5 Influence of Hyperparameters (RQ4)
4 Related Work
4.1 GCN-Based Recommendation
4.2 Cross-Domain Recommendation
4.3 Federated Cross-Domain Recommendation
5 Conclusion
References
Data-Agnostic Pivotal Instances Selection for Decision-Making Models
1 Introduction
2 Related Work
3 Pivot Tree Selection Model
4 Experiments
5 Conclusions
References
Disentangled Counterfactual Graph Augmentation Framework for Fair Graph Learning with Information Bottleneck
1 Introduction
2 Preliminaries
2.1 Notation
2.2 Fairness Metrics
2.3 Information Bottleneck
3 Counterfactual Graph Augmentation
4 Disentangled Counterfactual Augmenter Learning
4.1 Definition of FDGIB
4.2 Optimization of FDGIB
5 Counterfactual Augmentation and Fair Graph Learning
5.1 Counterfactual Augmentation with FDGIB
5.2 Fair Graph Learning with FDGIB
6 Experiments
6.1 Experiment Settings
6.2 Prediction Performance and Fairness (RQ 1)
6.3 Disentanglement Analysis (RQ 2)
6.4 Ablation Study(RQ 3)
6.5 Hyper-Parameter Sensitivity Analysis
7 Related Work
8 Conclusion
References
A New Framework for Evaluating the Validity and the Performance of Binary Decisions on Manifold-Valued Data
1 Introduction
2 Background
3 Formulation on the Tangent Space of the Sphere
4 The Finite-Dimensional Unit Sphere
4.1 Hypothesis Tests of a Single Spherical Mean
4.2 Hypothesis Tests for Equality of Two Spherical Means
5 The Manifold of PDFs
5.1 From Spherical Data to Distribution Functions
5.2 Hypothesis Tests
6 Experimental Results
6.1 Results on Simulated Data
6.2 Real Data
6.3 Comparison
7 Conclusion
References
The Future is Different: Predicting Reddits Popularity with Variational Dynamic Language Models
1 Introduction
2 Related Work
3 Model
4 Task, Dataset and Experimental Setup
5 Results and Discussion
6 Conclusion
References
CircuitVQA: A Visual Question Answering Dataset for Electrical Circuit Images
1 Introduction
2 Related Work
3 CircuitVQA Dataset Curation and Analysis
3.1 Collection of Circuit Images
3.2 Generation of Question Answer Pairs
3.3 CircuitVQA Dataset Analysis
4 Methods for CircuitVQA
4.1 Generative Models
4.2 Instruction Tuned Models
4.3 Language Modeling Loss
4.4 Input Representations
5 Experiments and Results
5.1 Experimental Setup
5.2 Metrics
5.3 Results
6 Conclusion
References
Author Index

System requirements

Save as PDF Copy link into clipboard

Schweitzer Fachinformationen

Machine Learning and Knowledge Discovery in Databases. Research Track

Description

More details

Other editions

Additional editions

Content

System requirements