Machine Learning and Principles and Practice of Knowledge Discovery in Databases

The five-volume set CCIS 2133-2137 constitutes the refereed proceedings of the workshops held in conjunction with the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, during September 18-22, 2023.

The 200 full papers presented in these proceedings were carefully reviewed and selected from 515 submissions. The papers have been organized in the following tracks:

Part I: Advances in Interpretable Machine Learning and Artificial Intelligence -- Joint Workshop and Tutorial; BIAS 2023 - 3rd Workshop on Bias and Fairness in AI; Biased Data in Conversational Agents; Explainable Artificial Intelligence: From Static to Dynamic; ML, Law and Society;

Part II: RKDE 2023: 1st International Tutorial and Workshop on Responsible Knowledge Discovery in Education; SoGood 2023 8th Workshop on Data Science for Social Good; Towards Hybrid Human-Machine Learning and Decision Making (HLDM); Uncertainty meets explainability in machine learning; Workshop: Deep Learning and Multimedia Forensics. Combating fake media and misinformation;

Part III: XAI-TS: Explainable AI for Time Series: Advances and Applications; XKDD 2023: 5th International Workshop on eXplainable Knowledge Discovery in Data Mining; Deep Learning for Sustainable Precision Agriculture; Knowledge Guided Machine Learning; MACLEAN: MAChine Learning for EArth ObservatioN; MLG: Mining and Learning with Graphs; Neuro Explicit AI and Expert Informed ML for Engineering and Physical Sciences; New Frontiers in Mining Complex Patterns;

Part IV: PharML, Machine Learning for Pharma and Healthcare Applications; Simplification, Compression, Efficiency and Frugality for Artificial intelligence; Workshop on Uplift Modeling and Causal Machine Learning for Operational Decision Making; 6th Workshop on AI in Aging, Rehabilitation and Intelligent Assisted Living (ARIAL); Adapting to Change: Reliable Multimodal Learning Across Domains; AI4M: AI for Manufacturing;

Part V: Challenges and Opportunities of Large Language Models in Real-World Machine Learning Applications; Deep learning meets Neuromorphic Hardware; Discovery challenge; ITEM: IoT, Edge, and Mobile for Embedded Machine Learning; LIMBO - LearnIng and Mining for BlOckchains; Machine Learning for Cybersecurity (MLCS 2023); MIDAS - The 8th Workshop on MIning DAta for financial applicationS; Workshop on Advancements in Federated Learning.

Contenu

.- PharML, Machine Learning for Pharma and Healthcare Applications.

.- CORKI: A Correlation-driven Imputation Method for Partial Annotation Scenarios in Multi-Label Clinical Problems.

.- Neuro-Symbolic Artificial Intelligence for Patient Monitoring.

.- Direct One-to-all Lead Conversion on 12-Lead Electrocardiogram.

.- Unveiling Driver Modules in Lung Cancer: A Clustering-Based Gene-Gene Interaction Network Analysis.

.- Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation.

.- Predicting Sepsis Onset with Deep Federated Learning.

.- A Workflow for Creating Multimodal Machine Learning Models for Metastasis Predictions in Melanoma Patients.

.- Molecular Fingerprints-based Machine Learning.

.- Simplification, Compression, Efficiency and Frugality for Artificial intelligence.

.- Neural Networks comprising Sequentially Semiseparable Matrices with one dimensional State Variable are Universal Approximators.

.- TinyMetaFed: Efficient Federated Meta-Learning for TinyML.

.- On The Potentials of Input Repetition in CNN Networks for Reducing Multiplications.

.- The Quest of Finding the Antidote to Sparse Double Descent.

.- Unveiling the Potential of Tiny Machine Learning for Enhanced People Counting in UWB Radar Data.

.- Towards Comparable Knowledge Distillation in Semantic Image Segmentation.

.- Combining Primal and Dual Representations in Deep Restricted Kernel Machines Classifiers.

.- Addressing limitations of TinyML approaches for AI-enabled Ambient Intelligence (Position Paper).

.- Leveraging low rank filters for efficient and knowledge-preserving lifelong learning.

.- Learning when to observe: A frugal reinforcement learning framework for a high-cost world.

.- Workshop on Uplift Modeling and Causal Machine Learning for Operational Decision Making.

.- Exploiting causal knowledge during CATE estimation using tree based metalearners.

.- A Parameter-Free Bayesian Framework for Uplift Modeling - Application on Telecom Data.

.- A churn prediction dataset from the telecom sector: a new benchmark for uplift modeling.

.- 6th Workshop on AI in Aging, Rehabilitation and Intelligent Assisted Living (ARIAL) .

.- Semi-Supervised Co-Teaching for Monitoring Parkinson's Disease Patients.

.- Explainable Artificial Intelligence in Medical Diagnostics: Insights into Alzheimer's Disease.

.- Cross-Modal Video to Body-joints Augmentation for Rehabilitation Exercise Quality Assessment.

.- Multimodal Sensor Fusion for Daily Living Activities Recognition in Active Assisted Living for Older Adults.

.- Modeling and Detecting Urinary Anomalies in Seniors from Data obtained by Unintrusive Sensors.

.- Assessing Frailty Using Behavioral and Physical Health Data in Everyday Living Settings.

.- Synthesizing Diabetic Foot Ulcer Images with Diffusion Model.

.- Engaging Older Adults at Meal-time through AI-empowered Socially Assistive Robots.

.- Investigating the Dynamics of Cardio-metabolic Comorbidities and their Interactions in Ageing Adults through Dynamic Bayesian Networks.

.- Adapting to Change: Reliable Multimodal Learning Across Domains.

.- Harnessing Error Patterns to Estimate Out-Of-Distribution Performance.

.- HAVE-Net: Hallucinated Audio-Visual Embeddings for Few-Shot Classification with Unimodal Cues.

.- CAD Models to Real-World Images: A Practical Approach to Unsupervised Domain Adaptation in Industrial Object Classification.

.- EMG subspace alignment and visualization for cross-subject hand gesture classification.

.- Adapting Classifiers To Changing Class Priors During Deployment.

.- AI4M: AI for Manufacturing.

.- Applying Machine Learning Models on Metrology Data for Predicting Device Electrical Performance.

.- Comparing Deep Reinforcement Learning Algorithms in Two-Echelon Supply Chains.

.- Reinforcement Learning for Segmented Manufacturing.

.- Automatic tool wear inspection by cascading sensor and image data.