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
Matchmaker: Self-Improving Large Language Model Programs for Schema Matching
Nabeel Seedat, Mihaela van der Schaar
Benchmark Data Repositories for Better Benchmarking
Rachel Longjohn, Markelle Kelly, Sameer Singh, Padhraic Smyth
Transformers to Predict the Applicability of Symbolic Integration Routines
Rashid Barket, Uzma Shafiq, Matthew England, Juergen Gerhard
Argumentation and Machine Learning
Antonio Rago, Kristijonas Čyras, Jack Mumford, Oana Cocarascu
EMGBench: Benchmarking Out-of-Distribution Generalization and Adaptation for Electromyography
Jehan Yang, Maxwell Soh, Vivianna Lieu, Douglas J Weber, Zackory Erickson
Assessing Concordance between RNA-Seq and NanoString Technologies in Ebola-Infected Nonhuman Primates Using Machine Learning
Mostafa Rezapour, Aarthi Narayanan, Wyatt H. Mowery, Metin Nafi Gurcan
Advancing Crime Linkage Analysis with Machine Learning: A Comprehensive Review and Framework for Data-Driven Approaches
Vinicius Lima, Umit Karabiyik
Profiling AI Models: Towards Efficient Computation Offloading in Heterogeneous Edge AI Systems
Juan Marcelo Parra-Ullauri, Oscar Dilley, Hari Madhukumar, Dimitra Simeonidou
Hyperparameter Optimization in Machine Learning
Luca Franceschi, Michele Donini, Valerio Perrone, Aaron Klein, Cédric Archambeau, Matthias Seeger, Massimiliano Pontil, Paolo Frasconi
Deep Learning and Machine Learning -- Natural Language Processing: From Theory to Application
Keyu Chen, Cheng Fei, Ziqian Bi, Junyu Liu, Benji Peng, Sen Zhang, Xuanhe Pan, Jiawei Xu, Jinlang Wang, Caitlyn Heqi Yin, Yichao Zhang, Pohsun Feng, Yizhu Wen, Tianyang Wang, Ming Li, Jintao Ren, Qian Niu, Silin Chen, Weiche Hsieh, Lawrence K.Q. Yan, Chia Xin Liang, Han Xu, Hong-Ming Tseng, Xinyuan Song, Ming Liu
Automated Trustworthiness Oracle Generation for Machine Learning Text Classifiers
Lam Nguyen Tung, Steven Cho, Xiaoning Du, Neelofar Neelofar, Valerio Terragni, Stefano Ruberto, Aldeida Aleti
A Systematic Review of Machine Learning in Sports Betting: Techniques, Challenges, and Future Directions
René Manassé Galekwa, Jean Marie Tshimula, Etienne Gael Tajeuna, Kyamakya Kyandoghere
$\texttt{skwdro}$: a library for Wasserstein distributionally robust machine learning
Florian Vincent, Waïss Azizian, Franck Iutzeler, Jérôme Malick
SPOTS-10: Animal Pattern Benchmark Dataset for Machine Learning Algorithms
John Atanbori
Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach
Firas Bayram, Bestoun S. Ahmed
FedCVD: The First Real-World Federated Learning Benchmark on Cardiovascular Disease Data
Yukun Zhang, Guanzhong Chen, Zenglin Xu, Jianyong Wang, Dun Zeng, Junfan Li, Jinghua Wang, Yuan Qi, Irwin King
Props for Machine-Learning Security
Ari Juels, Farinaz Koushanfar
When Less is More: Achieving Faster Convergence in Distributed Edge Machine Learning
Advik Raj Basani, Siddharth Chaitra Vivek, Advaith Krishna, Arnab K. Paul
CloudCast -- Total Cloud Cover Nowcasting with Machine Learning
Mikko Partio, Leila Hieta, Anniina Kokkonen