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
Applied Causal Inference Powered by ML and AI
Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, Vasilis Syrgkanis
Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection
Zhenglin Li, Haibei Zhu, Houze Liu, Jintong Song, Qishuo Cheng
Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission
Ziwen Wang, Jin Wee Lee, Tanujit Chakraborty, Yilin Ning, Mingxuan Liu, Feng Xie, Marcus Eng Hock Ong, Nan Liu
Robustness Bounds on the Successful Adversarial Examples: Theory and Practice
Hiroaki Maeshima, Akira Otsuka
NASH: Neural Architecture Search for Hardware-Optimized Machine Learning Models
Mengfei Ji, Yuchun Chang, Baolin Zhang, Zaid Al-Ars
Open-world Machine Learning: A Review and New Outlooks
Fei Zhu, Shijie Ma, Zhen Cheng, Xu-Yao Zhang, Zhaoxiang Zhang, Cheng-Lin Liu
Quantifying and Predicting Residential Building Flexibility Using Machine Learning Methods
Patrick Salter, Qiuhua Huang, Paulo Cesar Tabares-Velasco
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
Hyewon Jeong, Sarah Jabbour, Yuzhe Yang, Rahul Thapta, Hussein Mozannar, William Jongwon Han, Nikita Mehandru, Michael Wornow, Vladislav Lialin, Xin Liu, Alejandro Lozano, Jiacheng Zhu, Rafal Dariusz Kocielnik, Keith Harrigian, Haoran Zhang, Edward Lee, Milos Vukadinovic, Aparna Balagopalan, Vincent Jeanselme, Katherine Matton, Ilker Demirel, Jason Fries, Parisa Rashidi, Brett Beaulieu-Jones, Xuhai Orson Xu, Matthew McDermott, Tristan Naumann, Monica Agrawal, Marinka Zitnik, Berk Ustun, Edward Choi, Kristen Yeom, Gamze Gursoy, Marzyeh Ghassemi, Emma Pierson, George Chen, Sanjat Kanjilal, Michael Oberst, Linying Zhang, Harvineet Singh, Tom Hartvigsen, Helen Zhou, Chinasa T. Okolo
ML4PhySim : Machine Learning for Physical Simulations Challenge (The airfoil design)
Mouadh Yagoubi, Milad Leyli-Abadi, David Danan, Jean-Patrick Brunet, Jocelyn Ahmed Mazari, Florent Bonnet, Asma Farjallah, Marc Schoenauer, Patrick Gallinari
Machine Learning vs Deep Learning: The Generalization Problem
Yong Yi Bay, Kathleen A. Yearick
Limits to classification performance by relating Kullback-Leibler divergence to Cohen's Kappa
L. Crow, S. J. Watts
Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data
Bijan Roudini, Boshra Khajehpiri, Hamid Abrishami Moghaddam, Mohamad Forouzanfar
A Photonic Physically Unclonable Function's Resilience to Multiple-Valued Machine Learning Attacks
Jessie M. Henderson, Elena R. Henderson, Clayton A. Harper, Hiva Shahoei, William V. Oxford, Eric C. Larson, Duncan L. MacFarlane, Mitchell A. Thornton
Augmenting Automation: Intent-Based User Instruction Classification with Machine Learning
Lochan Basyal, Bijay Gaudel
Transfer Learning for Security: Challenges and Future Directions
Adrian Shuai Li, Arun Iyengar, Ashish Kundu, Elisa Bertino
Machine Learning Training Optimization using the Barycentric Correction Procedure
Sofia Ramos-Pulido, Neil Hernandez-Gress, Hector G. Ceballos-Cancino
Deciphering diffuse scattering with machine learning and the equivariant foundation model: The case of molten FeO
Ganesh Sivaraman, Chris J. Benmore