Nicosia / Ojha / Giesselbach Machine Learning, Optimization, and Data Science

10th International Conference, LOD 2024, Castiglione della Pescaia, Italy, September 22–25, 2024, Revised Selected Papers, Part I
Erscheinungsjahr 2025
ISBN: 978-3-031-82481-4
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 15508, 512 Seiten, eBook

Reihe: Lecture Notes in Computer Science

The three-volume set LNAI 15508-15510 constitutes the refereed proceedings of the 10th International Conference on Machine Learning, Optimization, and Data Science, LOD 2024, held in Castiglione della Pescaia, Italy, during September 22–25, 2024.This year, in the LOD Proceedings decided to also include the papers of the fourth edition of the Symposium on Artificial Intelligence and Neuroscience (ACAIN 2024).The 79 full papers included in this book were carefully reviewed and selected from 127 submissions. The LOD 2024 proceedings focus on machine learning, deep learning, AI, computational optimization, neuroscience and big data that includes invited talks, tutorial talks, special sessions, industrial tracks, demonstrations and oral and poster presentations of refereed papers.

Zielgruppe

Research

Autoren/Hrsg.

Nicosia, Giuseppe

Ojha, Varun

Giesselbach, Sven

Pardalos, M. Panos

Umeton, Renato

Inhaltsverzeichnis

.- Solving Two-Stage Stochastic Programming problems via Machine Learning.
.- Weight-varying Model Predictive Control for Coupled Cyber-Physical Systems: Aerial Grasping Study.
.- Assessing the Impact of Government Policies on Covid-19 Spread: A Machine Learning Approach.
.- Optimal Design and Implementation of an Open-source Emulation Platform for User-Centric Shared E-mobility Services.
.- Protein Sequence Generation using Denoising Probabilistic Diffusion Model.
.- Individual Fairness in Generative Text Models.
.- Refined Direct Preference Optimization with Synthetic Data for Behavioral Alignment of LLMs.
.- Artificial Intelligence and Cyber Security.
.- Exploring Digital Health Trends in the Headlines via Knowledge Graph Analysis.
.- Robust Infidelity: When Faithfulness Measures on Masked Language Models Are Misleading.
.- Optimal risk scores for continuous predictors.
.- Post-Treatment Gait Prediction after Botulinum Toxin Injections Using Deep Learning with an Attention Mechanism.
.- Leveraging Graph Networks and Generative Adversarial Networks for Controllable Trajectory Prediction.
.- Nearest Neighbors Counterfactuals.
.- An Attention-based Representation Distillation Baseline for Multi-Label Continual Learning.
.- Pattern detection in abnormal district heating data.
.- Harnessing Graph Neural Networks for Pattern Classification in Heterogeneous Event Graphs.
.- Learn to Create Neighborhoods in Real-World Vehicle Routing Problem.
.- PointerKex: A Pointer-based SSH Key Extraction method.
.- Addressing The Permutation Flowshop Scheduling with Grey Wolf Optimizer.
.- MCGRAN: Multi-Conditional Graph Generation for Neural Architecture Search.
.- Generative reward machine for Reinforcement learning for Physical Internet Distribution Centre.
.- Between accurate prediction and poor decision making: the AI/ML gap.
.- Cross-Metapath based Hashing for Recommendation Systems.
.- Beyond Iterative Tuning: Zero-Shot Hyperparameter Optimisation for Decision Trees.
.- Augmented Human-AI Forecasting for Ship Refit Project Scheduling: A Predict-then-Optimize Approach.
.- Evaluation of Document Deduplication Algorithms for Large Text Corpora.
.- Hicks Traverse meets One-Factor SVM: Belief Incoherence Attractors.
.- Synthetic Time Series for Anomaly Detection in Cloud Microservices.
.- Radiotherapy Treatment Planning: An Integrated Optimization and Reinforcement
Learning Approach.
.- Leap: Inductive Link Prediction via Learnable Topology Augmentation.
.- Estimating Completeness of Consensus Models: Geometrical and Distributional Approaches.
.- Active Inference Meeting Energy-Efficient Control of Parallel and Identical Machines.
.- Clarifying the Fog: Evaluating and Enhancing User Comprehension of Android Data Safety Documents.