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
CubicML: Automated ML for Large ML Systems Co-design with ML Prediction of Performance
Wei Wen, Quanyu Zhu, Weiwei Chu, Wen-Yen Chen, Jiyan Yang
Leveraging Machine Learning for Official Statistics: A Statistical Manifesto
Marco Puts, David Salgado, Piet Daas
Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression Labels
Emma Svensson, Hannah Rosa Friesacher, Susanne Winiwarter, Lewis Mervin, Adam Arany, Ola Engkvist
Understanding Data Importance in Machine Learning Attacks: Does Valuable Data Pose Greater Harm?
Rui Wen, Michael Backes, Yang Zhang
Threat Classification on Deployed Optical Networks Using MIMO Digital Fiber Sensing, Wavelets, and Machine Learning
Khouloud Abdelli, Henrique Pavani, Christian Dorize, Sterenn Guerrier, Haik Mardoyan, Patricia Layec, Jeremie Renaudier
Beyond Model Interpretability: Socio-Structural Explanations in Machine Learning
Andrew Smart, Atoosa Kasirzadeh
Efficient Multi-Task Large Model Training via Data Heterogeneity-aware Model Management
Yujie Wang, Shenhan Zhu, Fangcheng Fu, Xupeng Miao, Jie Zhang, Juan Zhu, Fan Hong, Yong Li, Bin Cui
Pricing American Options using Machine Learning Algorithms
Prudence Djagba, Callixte Ndizihiwe
Addressing the Gaps in Early Dementia Detection: A Path Towards Enhanced Diagnostic Models through Machine Learning
Juan A. Berrios Moya
ADHD diagnosis based on action characteristics recorded in videos using machine learning
Yichun Li, Syes Mohsen Naqvi, Rajesh Nair
Synthetic Data Generation and Automated Multidimensional Data Labeling for AI/ML in General and Circular Coordinates
Alice Williams, Boris Kovalerchuk
Counterfactual Fairness by Combining Factual and Counterfactual Predictions
Zeyu Zhou, Tianci Liu, Ruqi Bai, Jing Gao, Murat Kocaoglu, David I. Inouye
Learning Machines: In Search of a Concept Oriented Language
Veyis Gunes
Clustering of Indonesian and Western Gamelan Orchestras through Machine Learning of Performance Parameters
Simon Linke, Gerrit Wendt, Rolf Bader