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
Forecasting Four Business Cycle Phases Using Machine Learning: A Case Study of US and EuroZone
Elvys Linhares Pontes, Mohamed Benjannet, Raymond Yung
Performance evaluation of Reddit Comments using Machine Learning and Natural Language Processing methods in Sentiment Analysis
Xiaoxia Zhang, Xiuyuan Qi, Zixin Teng
Improving Simulation Regression Efficiency using a Machine Learning-based Method in Design Verification
Deepak Narayan Gadde, Sebastian Simon, Djones Lettnin, Thomas Ziller
Lost in the Averages: A New Specific Setup to Evaluate Membership Inference Attacks Against Machine Learning Models
Florent Guépin, Nataša Krčo, Matthieu Meeus, Yves-Alexandre de Montjoye
Analyzing the Impact of Climate Change With Major Emphasis on Pollution: A Comparative Study of ML and Statistical Models in Time Series Data
Anurag Mishra, Ronen Gold, Sanjeev Vijayakumar
Application of Machine Learning in Agriculture: Recent Trends and Future Research Avenues
Aashu, Kanchan Rajwar, Millie Pant, Kusum Deep
Kinetics of orbital ordering in cooperative Jahn-Teller models: Machine-learning enabled large-scale simulations
Supriyo Ghosh, Sheng Zhang, Chen Cheng, Gia-Wei Chern
Fault Tolerant ML: Efficient Meta-Aggregation and Synchronous Training
Tehila Dahan, Kfir Y. Levy
A Systematic and Formal Study of the Impact of Local Differential Privacy on Fairness: Preliminary Results
Karima Makhlouf, Tamara Stefanovic, Heber H. Arcolezi, Catuscia Palamidessi
Defining error accumulation in ML atmospheric simulators
Raghul Parthipan, Mohit Anand, Hannah M. Christensen, J. Scott Hosking, Damon J. Wischik
Unraveling overoptimism and publication bias in ML-driven science
Pouria Saidi, Gautam Dasarathy, Visar Berisha
FloodDamageCast: Building Flood Damage Nowcasting with Machine Learning and Data Augmentation
Chia-Fu Liu, Lipai Huang, Kai Yin, Sam Brody, Ali Mostafavi
Online Classification with Predictions
Vinod Raman, Ambuj Tewari
A Dynamic Model of Performative Human-ML Collaboration: Theory and Empirical Evidence
Tom Sühr, Samira Samadi, Chiara Farronato
Why do explanations fail? A typology and discussion on failures in XAI
Clara Bove, Thibault Laugel, Marie-Jeanne Lesot, Charles Tijus, Marcin Detyniecki