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
Machine learning and domain decomposition methods -- a survey
Axel Klawonn, Martin Lanser, Janine Weber
Noninvasive Estimation of Mean Pulmonary Artery Pressure Using MRI, Computer Models, and Machine Learning
Michal K. Grzeszczyk, Tadeusz Satlawa, Angela Lungu, Andrew Swift, Andrew Narracott, Rod Hose, Tomasz Trzcinski, Arkadiusz Sitek
The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction
Pratyusha Sharma, Jordan T. Ash, Dipendra Misra
Machine Learning and Feature Ranking for Impact Fall Detection Event Using Multisensor Data
Tresor Y. Koffi, Youssef Mourchid, Mohammed Hindawi, Yohan Dupuis
Clustering and Uncertainty Analysis to Improve the Machine Learning-based Predictions of SAFARI-1 Control Follower Assembly Axial Neutron Flux Profiles
Lesego Moloko, Pavel Bokov, Xu Wu, Kostadin Ivanov
Position Paper: Bridging the Gap Between Machine Learning and Sensitivity Analysis
Christian A. Scholbeck, Julia Moosbauer, Giuseppe Casalicchio, Hoshin Gupta, Bernd Bischl, Christian Heumann
Pyreal: A Framework for Interpretable ML Explanations
Alexandra Zytek, Wei-En Wang, Dongyu Liu, Laure Berti-Equille, Kalyan Veeramachaneni
Machine Learning for Anomaly Detection in Particle Physics
Vasilis Belis, Patrick Odagiu, Thea Klæboe Årrestad
CodeLL: A Lifelong Learning Dataset to Support the Co-Evolution of Data and Language Models of Code
Martin Weyssow, Claudio Di Sipio, Davide Di Ruscio, Houari Sahraoui
Studying the Practices of Testing Machine Learning Software in the Wild
Moses Openja, Foutse Khomh, Armstrong Foundjem, Zhen Ming, Jiang, Mouna Abidi, Ahmed E. Hassan
Robust Machine Learning by Transforming and Augmenting Imperfect Training Data
Elliot Creager
Inferring the relationship between soil temperature and the normalized difference vegetation index with machine learning
Steven Mortier, Amir Hamedpour, Bart Bussmann, Ruth Phoebe Tchana Wandji, Steven Latré, Bjarni D. Sigurdsson, Tom De Schepper, Tim Verdonck
Decentralised and collaborative machine learning framework for IoT
Martín González-Soto, Rebeca P. Díaz-Redondo, Manuel Fernández-Veiga, Bruno Rodríguez-Castro, Ana Fernández-Vilas
Sign Language Conversation Interpretation Using Wearable Sensors and Machine Learning
Basma Kalandar, Ziemowit Dworakowski
Scope Compliance Uncertainty Estimate
Al-Harith Farhad, Ioannis Sorokos, Mohammed Naveed Akram, Koorosh Aslansefat, Daniel Schneider
Discretionary Trees: Understanding Street-Level Bureaucracy via Machine Learning
Gaurab Pokharel, Sanmay Das, Patrick J. Fowler
Android Malware Detection with Unbiased Confidence Guarantees
Harris Papadopoulos, Nestoras Georgiou, Charalambos Eliades, Andreas Konstantinidis
SAME: Sample Reconstruction against Model Extraction Attacks
Yi Xie, Jie Zhang, Shiqian Zhao, Tianwei Zhang, Xiaofeng Chen