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
A Systematic Review of Machine Learning Approaches for Detecting Deceptive Activities on Social Media: Methods, Challenges, and Biases
Yunchong Liu, Xiaorui Shen, Yeyubei Zhang, Zhongyan Wang, Yexin Tian, Jianglai Dai, Yuchen Cao
Proactive Fraud Defense: Machine Learning's Evolving Role in Protecting Against Online Fraud
Md Kamrul Hasan Chy
Mechanism learning: Reverse causal inference in the presence of multiple unknown confounding through front-door causal bootstrapping
Jianqiao Mao, Max A. Little
Recommendations for Comprehensive and Independent Evaluation of Machine Learning-Based Earth System Models
Paul A. Ullrich, Elizabeth A. Barnes, William D. Collins, Katherine Dagon, Shiheng Duan, Joshua Elms, Jiwoo Lee, L. Ruby Leung, Dan Lu, Maria J. Molina, Travis A. O'Brien
Exploring the Universe with SNAD: Anomaly Detection in Astronomy
Alina A. Volnova, Patrick D. Aleo, Anastasia Lavrukhina, Etienne Russeil, Timofey Semenikhin, Emmanuel Gangler, Emille E. O. Ishida, Matwey V. Kornilov, Vladimir Korolev, Konstantin Malanchev, Maria V. Pruzhinskaya, Sreevarsha Sreejith
Adapting MLOps for Diverse In-Network Intelligence in 6G Era: Challenges and Solutions
Peizheng Li, Ioannis Mavromatis, Tim Farnham, Adnan Aijaz, Aftab Khan
Harnessing PU Learning for Enhanced Cloud-based DDoS Detection: A Comparative Analysis
Robert Dilworth, Charan Gudla
Assessing Alcohol Use Disorder: Insights from Lifestyle, Background, and Family History with Machine Learning Techniques
Chenlan Wang, Gaojian Huang, Yue Luo
SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning
Yizhou Chi, Yizhang Lin, Sirui Hong, Duyi Pan, Yaying Fei, Guanghao Mei, Bangbang Liu, Tianqi Pang, Jacky Kwok, Ceyao Zhang, Bang Liu, Chenglin Wu
Deep Learning and Machine Learning -- Python Data Structures and Mathematics Fundamental: From Theory to Practice
Silin Chen, Ziqian Bi, Junyu Liu, Benji Peng, Sen Zhang, Xuanhe Pan, Jiawei Xu, Jinlang Wang, Keyu Chen, Caitlyn Heqi Yin, Pohsun Feng, Yizhu Wen, Tianyang Wang, Ming Li, Jintao Ren, Qian Niu, Ming Liu
Forecasting Opioid Incidents for Rapid Actionable Data for Opioid Response in Kentucky
Aaron D. Mullen, Daniel Harris, Peter Rock, Svetla Slavova, Jeffery Talbert, V.K. Cody Bumgardner
Systematic Review: Text Processing Algorithms in Machine Learning and Deep Learning for Mental Health Detection on Social Media
Yuchen Cao, Jianglai Dai, Zhongyan Wang, Yeyubei Zhang, Xiaorui Shen, Yunchong Liu, Yexin Tian
A Data-driven Crowd Simulation Framework Integrating Physics-informed Machine Learning with Navigation Potential Fields
Runkang Guo, Bin Chen, Qi Zhang, Yong Zhao, Xiao Wang, Zhengqiu Zhu
Integer linear programming for unsupervised training set selection in molecular machine learning
Matthieu Haeberle, Puck van Gerwen, Ruben Laplaza, Ksenia R. Briling, Jan Weinreich, Friedrich Eisenbrand, Clemence Corminboeuf
Redefining Finance: The Influence of Artificial Intelligence (AI) and Machine Learning (ML)
Animesh Kumar