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
Review Non-convex Optimization Method for Machine Learning
Greg B Fotopoulos, Paul Popovich, Nicholas Hall Papadopoulos
shapiq: Shapley Interactions for Machine Learning
Maximilian Muschalik, Hubert Baniecki, Fabian Fumagalli, Patrick Kolpaczki, Barbara Hammer, Eyke Hüllermeier
Causal Inference Tools for a Better Evaluation of Machine Learning
Michaël Soumm
A Federated Learning Platform as a Service for Advancing Stroke Management in European Clinical Centers
Diogo Reis Santos, Albert Sund Aillet, Antonio Boiano, Usevalad Milasheuski, Lorenzo Giusti, Marco Di Gennaro, Sanaz Kianoush, Luca Barbieri, Monica Nicoli, Michele Carminati, Alessandro E. C. Redondi, Stefano Savazzi, Luigi Serio
Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Unveiling AI's Potential Through Tools, Techniques, and Applications
Pohsun Feng, Ziqian Bi, Yizhu Wen, Xuanhe Pan, Benji Peng, Ming Liu, Jiawei Xu, Keyu Chen, Junyu Liu, Caitlyn Heqi Yin, Sen Zhang, Jinlang Wang, Qian Niu, Ming Li, Tianyang Wang
Enhancing End Stage Renal Disease Outcome Prediction: A Multi-Sourced Data-Driven Approach
Yubo Li, Rema Padman
GalaxiesML: a dataset of galaxy images, photometry, redshifts, and structural parameters for machine learning
Tuan Do (1), Bernie Boscoe (2), Evan Jones (1), Yun Qi Li (1, 3), Kevin Alfaro (1) ((1) UCLA, (2) Southern Oregon University, (3) University of Washington)
CliMB: An AI-enabled Partner for Clinical Predictive Modeling
Evgeny Saveliev, Tim Schubert, Thomas Pouplin, Vasilis Kosmoliaptsis, Mihaela van der Schaar
A Knowledge-Informed Large Language Model Framework for U.S. Nuclear Power Plant Shutdown Initiating Event Classification for Probabilistic Risk Assessment
Min Xian, Tao Wang, Sai Zhang, Fei Xu, Zhegang Ma
Using fractal dimension to predict the risk of intra cranial aneurysm rupture with machine learning
Pradyumna Elavarthi, Anca Ralescu, Mark D. Johnson, Charles J. Prestigiacomo
SMLE: Safe Machine Learning via Embedded Overapproximation
Matteo Francobaldi, Michele Lombardi
Novel machine learning applications at the LHC
Javier M. Duarte
Machine Learning in Industrial Quality Control of Glass Bottle Prints
Maximilian Bundscherer, Thomas H. Schmitt, Tobias Bocklet
Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Object-Oriented Programming
Ming Li, Ziqian Bi, Tianyang Wang, Keyu Chen, Jiawei Xu, Qian Niu, Junyu Liu, Benji Peng, Sen Zhang, Xuanhe Pan, Jinlang Wang, Pohsun Feng, Caitlyn Heqi Yin, Yizhu Wen, Ming Liu
ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilities
Ezra Karger, Houtan Bastani, Chen Yueh-Han, Zachary Jacobs, Danny Halawi, Fred Zhang, Philip E. Tetlock