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
An Algorithm-Centered Approach To Model Streaming Data
Fabian Hinder, Valerie Vaquet, David Komnick, Barbara Hammer
Beyond Confusion: A Fine-grained Dialectical Examination of Human Activity Recognition Benchmark Datasets
Daniel Geissler, Dominique Nshimyimana, Vitor Fortes Rey, Sungho Suh, Bo Zhou, Paul Lukowicz
Pulling the Carpet Below the Learner's Feet: Genetic Algorithm To Learn Ensemble Machine Learning Model During Concept Drift
Teddy Lazebnik
Regional Weather Variable Predictions by Machine Learning with Near-Surface Observational and Atmospheric Numerical Data
Yihe Zhang, Bryce Turney, Purushottam Sigdel, Xu Yuan, Eric Rappin, Adrian Lago, Sytske Kimball, Li Chen, Paul Darby, Lu Peng, Sercan Aygun, Yazhou Tu, M. Hassan Najafi, Nian-Feng Tzeng
A Beginner's Guide to Power and Energy Measurement and Estimation for Computing and Machine Learning
Akshaya Jagannadharao, Nicole Beckage, Sovan Biswas, Hilary Egan, Jamil Gafur, Thijs Metsch, Dawn Nafus, Giuseppe Raffa, Charles Tripp
Discover Physical Concepts and Equations with Machine Learning
Bao-Bing Li, Yi Gu, Shao-Feng Wu
Backdoor attacks on DNN and GBDT -- A Case Study from the insurance domain
Robin Kühlem (1), Daniel Otten (1), Daniel Ludwig (1), Anselm Hudde (1 and 3), Alexander Rosenbaum (2), Andreas Mauthe (2) ((1) Debeka, Koblenz, Germany, (2) Computer Science, University of Koblenz, Koblenz, Germany, (3) Department of Maths and Technology, Koblenz University of Applied Sciences, Remagen, Germany)
Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Software
Zhenpeng Chen, Xinyue Li, Jie M. Zhang, Federica Sarro, Yang Liu
FuzzDistill: Intelligent Fuzzing Target Selection using Compile-Time Analysis and Machine Learning
Saket Upadhyay
Quantum-Cognitive Neural Networks: Assessing Confidence and Uncertainty with Human Decision-Making Simulations
Milan Maksimovic, Ivan S. Maksymov
Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy, Research, and Practice
A. Feder Cooper, Christopher A. Choquette-Choo, Miranda Bogen, Matthew Jagielski, Katja Filippova, Ken Ziyu Liu, Alexandra Chouldechova, Jamie Hayes, Yangsibo Huang, Niloofar Mireshghallah, Ilia Shumailov, Eleni Triantafillou, Peter Kairouz, Nicole Mitchell, Percy Liang, Daniel E. Ho, Yejin Choi, Sanmi Koyejo, Fernando Delgado, James Grimmelmann, Vitaly Shmatikov, Christopher De Sa, Solon Barocas, Amy Cyphert, Mark Lemley, danah boyd, Jennifer Wortman Vaughan, Miles Brundage, David Bau, Seth Neel, Abigail Z. Jacobs, Andreas Terzis, Hanna Wallach, Nicolas Papernot, Katherine Lee
Vulnerability of Text-Matching in ML/AI Conference Reviewer Assignments to Collusions
Jhih-Yi (Janet) Hsieh, Aditi Raghunathan, Nihar B. Shah
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning
Yuanyuan Wang, Qian Song, Dawood Wasif, Muhammad Shahzad, Christoph Koller, Jonathan Bamber, Xiao Xiang Zhu
Safety Monitoring of Machine Learning Perception Functions: a Survey
Raul Sena Ferreira, Joris Guérin, Kevin Delmas, Jérémie Guiochet, Hélène Waeselynck
Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder
Kamala Devi Kannan, Senthil Kumar Jagatheesaperumal, Rajesh N. V. P. S. Kandala, Mojtaba Lotfaliany, Roohallah Alizadehsanid, Mohammadreza Mohebbi
Order Theory in the Context of Machine Learning: an application
Eric Dolores-Cuenca, Aldo Guzman-Saenz, Sangil Kim, Susana Lopez-Moreno, Jose Mendoza-Cortes
Ethnography and Machine Learning: Synergies and New Directions
Zhuofan Li, Corey M. Abramson
Towards Modeling Data Quality and Machine Learning Model Performance
Usman Anjum, Chris Trentman, Elrod Caden, Justin Zhan
Risk factor identification and classification of malnutrition among under-five children in Bangladesh: Machine learning and statistical approach
Tasfin Mahmud, Tayab Uddin Wara, Chironjeet Das Joy