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 Theory of Machine Learning
Jinsook Kim, Jinho Kang
Experiments with truth using Machine Learning: Spectral analysis and explainable classification of synthetic, false, and genuine information
Vishnu S. Pendyala, Madhulika Dutta
Quantum Dynamics of Machine Learning
Peng Wang, Maimaitiniyazi Maimaitiabudula
Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach
Oladipo A. Madamidola, Felix Ngobigha, Adnane Ez-zizi
Amazing Things Come From Having Many Good Models
Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, Zachery Boner
An AI Architecture with the Capability to Classify and Explain Hardware Trojans
Paul Whitten, Francis Wolff, Chris Papachristou
Introducing 'Inside' Out of Distribution
Teddy Lazebnik
Machine Learning for Complex Systems with Abnormal Pattern by Exception Maximization Outlier Detection Method
Zhikun Zhang, Yiting Duan, Xiangjun Wang, Mingyuan Zhang
A Critical Assessment of Interpretable and Explainable Machine Learning for Intrusion Detection
Omer Subasi, Johnathan Cree, Joseph Manzano, Elena Peterson
Bias Correction in Machine Learning-based Classification of Rare Events
Luuk Gubbels, Marco Puts, Piet Daas
Machine Learning for Economic Forecasting: An Application to China's GDP Growth
Yanqing Yang, Xingcheng Xu, Jinfeng Ge, Yan Xu
Advanced Smart City Monitoring: Real-Time Identification of Indian Citizen Attributes
Shubham Kale, Shashank Sharma, Abhilash Khuntia
Can machine learning solve the challenge of adaptive learning and the individualization of learning paths? A field experiment in an online learning platform
Tim Klausmann, Marius Köppel, Daniel Schunk, Isabell Zipperle
How Reliable and Stable are Explanations of XAI Methods?
José Ribeiro, Lucas Cardoso, Vitor Santos, Eduardo Carvalho, Níkolas Carneiro, Ronnie Alves
A Geometric Framework for Adversarial Vulnerability in Machine Learning
Brian Bell
The More the Merrier? Navigating Accuracy vs. Energy Efficiency Design Trade-Offs in Ensemble Learning Systems
Rafiullah Omar, Justus Bogner, Henry Muccini, Patricia Lago, Silverio Martínez-Fernández, Xavier Franch
Precise and Efficient Orbit Prediction in LEO with Machine Learning using Exogenous Variables
Francisco Caldas, Cláudia Soares
Development of Machine Learning Classifiers for Blood-based Diagnosis and Prognosis of Suspected Acute Infections and Sepsis
Ljubomir Buturovic, Michael Mayhew, Roland Luethy, Kirindi Choi, Uros Midic, Nandita Damaraju, Yehudit Hasin-Brumshtein, Amitesh Pratap, Rhys M. Adams, Joao Fonseca, Ambika Srinath, Paul Fleming, Claudia Pereira, Oliver Liesenfeld, Purvesh Khatri, Timothy Sweeney
Artificial intelligence and machine learning generated conjectures with TxGraffiti
Randy Davila