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
Short text classification with machine learning in the social sciences: The case of climate change on Twitter
Karina Shyrokykh, Maksym Girnyk, Lisa Dellmuth
Machine learning assist nyc subway navigation safer and faster
Wencheng Bao, Shi Feng
Secure and Effective Data Appraisal for Machine Learning
Xu Ouyang, Changhong Yang, Felix Xiaozhu Lin, Yangfeng Ji
Automatic Quality Assessment of Wikipedia Articles -- A Systematic Literature Review
Pedro Miguel Moás, Carla Teixeira Lopes
Generalized Convergence Analysis of Tsetlin Machines: A Probabilistic Approach to Concept Learning
Mohamed-Bachir Belaid, Jivitesh Sharma, Lei Jiao, Ole-Christoffer Granmo, Per-Arne Andersen, Anis Yazidi
Data Cleaning and Machine Learning: A Systematic Literature Review
Pierre-Olivier Côté, Amin Nikanjam, Nafisa Ahmed, Dmytro Humeniuk, Foutse Khomh
A Framework for Interpretability in Machine Learning for Medical Imaging
Alan Q. Wang, Batuhan K. Karaman, Heejong Kim, Jacob Rosenthal, Rachit Saluja, Sean I. Young, Mert R. Sabuncu
Artemis: HE-Aware Training for Efficient Privacy-Preserving Machine Learning
Yeonsoo Jeon, Mattan Erez, Michael Orshansky
A Robust Machine Learning Approach for Path Loss Prediction in 5G Networks with Nested Cross Validation
Ibrahim Yazıcı, Emre Gures
Can Pre-trained Networks Detect Familiar Out-of-Distribution Data?
Atsuyuki Miyai, Qing Yu, Go Irie, Kiyoharu Aizawa
Machine Learning for Practical Quantum Error Mitigation
Haoran Liao, Derek S. Wang, Iskandar Sitdikov, Ciro Salcedo, Alireza Seif, Zlatko K. Minev
Leave-one-out Distinguishability in Machine Learning
Jiayuan Ye, Anastasia Borovykh, Soufiane Hayou, Reza Shokri
Beyond Tides and Time: Machine Learning Triumph in Water Quality
Yinpu Li, Siqi Mao, Yaping Yuan, Ziren Wang, Yixin Kang, Yuanxin Yao
Cross-Prediction-Powered Inference
Tijana Zrnic, Emmanuel J. Candès
Collaborative Distributed Machine Learning
David Jin, Niclas Kannengießer, Sascha Rank, Ali Sunyaev
Review of Machine Learning Methods for Additive Manufacturing of Functionally Graded Materials
Mohammad Karimzadeh, Aleksandar Vakanski, Fei Xu, Xinchang Zhang
Unmasking the Chameleons: A Benchmark for Out-of-Distribution Detection in Medical Tabular Data
Mohammad Azizmalayeri, Ameen Abu-Hanna, Giovanni Ciná
Systematic Sampling and Validation of Machine Learning-Parameterizations in Climate Models
Jerry Lin, Sungduk Yu, Tom Beucler, Pierre Gentine, David Walling, Mike Pritchard