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
Generalization vs. Specialization under Concept Shift
Alex Nguyen, David J. Schwab, Vudtiwat Ngampruetikorn
Stalactite: Toolbox for Fast Prototyping of Vertical Federated Learning Systems
Anastasiia Zakharova, Dmitriy Alexandrov, Maria Khodorchenko, Nikolay Butakov, Alexey Vasilev, Maxim Savchenko, Alexander Grigorievskiy
Identification and Localization of Cometary Activity in Solar System Objects with Machine Learning
Bryce T. Bolin, Michael W. Coughlin
Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling
Lechao Xiao
AdapFair: Ensuring Continuous Fairness for Machine Learning Operations
Yinghui Huang, Zihao Tang, Xiangyu Chang
Efficient Tabular Data Preprocessing of ML Pipelines
Yu Zhu, Wenqi Jiang, Gustavo Alonso
Research on Dynamic Data Flow Anomaly Detection based on Machine Learning
Liyang Wang, Yu Cheng, Hao Gong, Jiacheng Hu, Xirui Tang, Iris Li
Explainable AI needs formal notions of explanation correctness
Stefan Haufe, Rick Wilming, Benedict Clark, Rustam Zhumagambetov, Danny Panknin, Ahcène Boubekki
Prediction and Detection of Terminal Diseases Using Internet of Medical Things: A Review
Akeem Temitope Otapo, Alice Othmani, Ghazaleh Khodabandelou, Zuheng Ming
Causal Inference with Double/Debiased Machine Learning for Evaluating the Health Effects of Multiple Mismeasured Pollutants
Gang Xu, Xin Zhou, Molin Wang, Boya Zhang, Wenhao Jiang, Francine Laden, Helen H. Suh, Adam A. Szpiro, Donna Spiegelman, Zuoheng Wang
Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Tensorflow Pretrained Models
Keyu Chen, Ziqian Bi, Qian Niu, Junyu Liu, Benji Peng, Sen Zhang, Ming Liu, Ming Li, Xuanhe Pan, Jiawei Xu, Jinlang Wang, Pohsun Feng
Data Compression using Rank-1 Lattices for Parameter Estimation in Machine Learning
Michael Gnewuch, Kumar Harsha, Marcin Wnuk
Multi-omics data integration for early diagnosis of hepatocellular carcinoma (HCC) using machine learning
Annette Spooner, Mohammad Karimi Moridani, Azadeh Safarchi, Salim Maher, Fatemeh Vafaee, Amany Zekry, Arcot Sowmya
Predicting DNA fragmentation: A non-destructive analogue to chemical assays using machine learning
Byron A Jacobs, Ifthakaar Shaik, Frando Lin
Predicting soccer matches with complex networks and machine learning
Eduardo Alves Baratela, Felipe Jordão Xavier, Thomas Peron, Paulino Ribeiro Villas-Boas, Francisco Aparecido Rodrigues
Impact of ML Optimization Tactics on Greener Pre-Trained ML Models
Alexandra González Álvarez, Joel Castaño, Xavier Franch, Silverio Martínez-Fernández