Machine Learning
Machine learning (ML) focuses on developing algorithms that allow computers to learn from data without explicit programming, aiming to improve prediction accuracy, automate tasks, and extract insights. Current research emphasizes areas like fairness in federated learning, efficient model training and deployment (including techniques to reduce communication overhead), and enhancing model interpretability and robustness against adversarial attacks. ML's impact spans diverse fields, from healthcare (e.g., disease prediction) and industrial quality control to astrophysics (e.g., galaxy classification) and cybersecurity, demonstrating its broad applicability and significant potential for scientific advancement and practical problem-solving.
Papers
Oblivious Defense in ML Models: Backdoor Removal without Detection
Shafi Goldwasser, Jonathan Shafer, Neekon Vafa, Vinod Vaikuntanathan
Graph-Based Semi-Supervised Segregated Lipschitz Learning
Farid Bozorgnia, Yassine Belkheiri, Abderrahim Elmoataz
Evaluating Machine Learning Models against Clinical Protocols for Enhanced Interpretability and Continuity of Care
Christel Sirocchi, Muhammad Suffian, Federico Sabbatini, Alessandro Bogliolo, Sara Montagna
[Vision Paper] PRObot: Enhancing Patient-Reported Outcome Measures for Diabetic Retinopathy using Chatbots and Generative AI
Maren Pielka, Tobias Schneider, Jan Terheyden, Rafet Sifa
Visually Analyze SHAP Plots to Diagnose Misclassifications in ML-based Intrusion Detection
Maraz Mia, Mir Mehedi A. Pritom, Tariqul Islam, Kamrul Hasan
Machine learning identification of maternal inflammatory response and histologic choroamnionitis from placental membrane whole slide images
Abhishek Sharma, Ramin Nateghi, Marina Ayad, Lee A.D. Cooper, Jeffery A. Goldstein
Federated GNNs for EEG-Based Stroke Assessment
Andrea Protani, Lorenzo Giusti, Albert Sund Aillet, Chiara Iacovelli, Giuseppe Reale, Simona Sacco, Paolo Manganotti, Lucio Marinelli, Diogo Reis Santos, Pierpaolo Brutti, Pietro Caliandro, Luigi Serio
An Exponential Separation Between Quantum and Quantum-Inspired Classical Algorithms for Machine Learning
Allan Grønlund, Kasper Green Larsen
Towards certification: A complete statistical validation pipeline for supervised learning in industry
Lucas Lacasa, Abel Pardo, Pablo Arbelo, Miguel Sánchez, Pablo Yeste, Noelia Bascones, Alejandro Martínez-Cava, Gonzalo Rubio, Ignacio Gómez, Eusebio Valero, Javier de Vicente
Equitable Length of Stay Prediction for Patients with Learning Disabilities and Multiple Long-term Conditions Using Machine Learning
Emeka Abakasanga, Rania Kousovista, Georgina Cosma, Ashley Akbari, Francesco Zaccardi, Navjot Kaur, Danielle Fitt, Gyuchan Thomas Jun, Reza Kiani, Satheesh Gangadharan
Machine Learning Innovations in CPR: A Comprehensive Survey on Enhanced Resuscitation Techniques
Saidul Islam, Gaith Rjoub, Hanae Elmekki, Jamal Bentahar, Witold Pedrycz, Robin Cohen
A Machine Learning based Hybrid Receiver for 5G NR PRACH
Rohit Singh, Anil Kumar Yerrapragada, Radha Krishna Ganti
Effective ML Model Versioning in Edge Networks
Fin Gentzen, Mounir Bensalem, Admela Jukan
A Similarity-Based Oversampling Method for Multi-label Imbalanced Text Data
Ismail Hakki Karaman, Gulser Koksal, Levent Eriskin, Salih Salihoglu
Ratio law: mathematical descriptions for a universal relationship between AI performance and input samples
Boming Kang, Qinghua Cui
Analysis of ELSA COVID-19 Substudy response rate using machine learning algorithms
Marjan Qazvini
Improving Musical Instrument Classification with Advanced Machine Learning Techniques
Joanikij Chulev