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
How do Machine Learning Models Change?
Joel Castaño, Rafael Cabañas, Antonio Salmerón, David Lo, Silverio Martínez-Fernández
Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning
Jun Qi, Chao-Han Yang, Samuel Yen-Chi Chen, Pin-Yu Chen
Enhancing generalization in high energy physics using white-box adversarial attacks
Franck Rothen, Samuel Klein, Matthew Leigh, Tobias Golling
Optimisation Strategies for Ensuring Fairness in Machine Learning: With and Without Demographics
Quan Zhou
SAFELOC: Overcoming Data Poisoning Attacks in Heterogeneous Federated Machine Learning for Indoor Localization
Akhil Singampalli, Danish Gufran, Sudeep Pasricha
Mapping Methane -- The Impact of Dairy Farm Practices on Emissions Through Satellite Data and Machine Learning
Hanqing Bi, Suresh Neethirajan
Machine learning approaches to explore important features behind bird flight modes
Yukino Kawai, Tatsuya Hisada, Kozue Shiomi, Momoko Hayamizu
Intelligent Algorithms For Signature Diagnostics Of Three-Phase Motors
Stepan Svirin, Artem Ryzhikov, Saraa Ali, Denis Derkach
Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey
Yang Gu, Hengyu You, Jian Cao, Muran Yu, Haoran Fan, Shiyou Qian
Which PPML Would a User Choose? A Structured Decision Support Framework for Developers to Rank PPML Techniques Based on User Acceptance Criteria
Sascha Löbner, Sebastian Pape, Vanessa Bracamonte, Kittiphop Phalakarn
Enhancing Phishing Detection through Feature Importance Analysis and Explainable AI: A Comparative Study of CatBoost, XGBoost, and EBM Models
Abdullah Fajar, Setiadi Yazid, Indra Budi
Machine learning enabled velocity model building with uncertainty quantification
Rafael Orozco, Huseyin Tuna Erdinc, Yunlin Zeng, Mathias Louboutin, Felix J. Herrmann
A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning
Caleb J.S. Barr, Olivia Erdelyi, Paul D. Docherty, Randolph C. Grace
Gen-AI for User Safety: A Survey
Akshar Prabhu Desai, Tejasvi Ravi, Mohammad Luqman, Mohit Sharma, Nithya Kota, Pranjul Yadav
MBL-CPDP: A Multi-objective Bilevel Method for Cross-Project Defect Prediction via Automated Machine Learning
Jiaxin Chen, Jinliang Ding, Kay Chen Tan, Jiancheng Qian, Ke Li
Prompts Matter: Comparing ML/GAI Approaches for Generating Inductive Qualitative Coding Results
John Chen, Alexandros Lotsos, Lexie Zhao, Grace Wang, Uri Wilensky, Bruce Sherin, Michael Horn