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
Fast yet Safe: Early-Exiting with Risk Control
Metod Jazbec, Alexander Timans, Tin Hadži Veljković, Kaspar Sakmann, Dan Zhang, Christian A. Naesseth, Eric Nalisnick
"Forgetting" in Machine Learning and Beyond: A Survey
Alyssa Shuang Sha, Bernardo Pereira Nunes, Armin Haller
Class-Based Time Series Data Augmentation to Mitigate Extreme Class Imbalance for Solar Flare Prediction
Junzhi Wen, Rafal A. Angryk
A Novel Review of Stability Techniques for Improved Privacy-Preserving Machine Learning
Coleman DuPlessie, Aidan Gao
Policy Trees for Prediction: Interpretable and Adaptive Model Selection for Machine Learning
Dimitris Bertsimas, Matthew Peroni
Scaling Laws for the Value of Individual Data Points in Machine Learning
Ian Covert, Wenlong Ji, Tatsunori Hashimoto, James Zou
Understanding Encoder-Decoder Structures in Machine Learning Using Information Measures
Jorge F. Silva, Victor Faraggi, Camilo Ramirez, Alvaro Egana, Eduardo Pavez
The Impact of Ontology on the Prediction of Cardiovascular Disease Compared to Machine Learning Algorithms
Hakim El Massari, Noreddine Gherabi, Sajida Mhammedi, Hamza Ghandi, Mohamed Bahaj, Muhammad Raza Naqvi
Federated and Transfer Learning for Cancer Detection Based on Image Analysis
Amine Bechar, Youssef Elmir, Yassine Himeur, Rafik Medjoudj, Abbes Amira
A Staged Approach using Machine Learning and Uncertainty Quantification to Predict the Risk of Hip Fracture
Anjum Shaik, Kristoffer Larsen, Nancy E. Lane, Chen Zhao, Kuan-Jui Su, Joyce H. Keyak, Qing Tian, Qiuying Sha, Hui Shen, Hong-Wen Deng, Weihua Zhou
Task-Agnostic Machine-Learning-Assisted Inference
Jiacheng Miao, Qiongshi Lu
Counterfactual Explanations for Multivariate Time-Series without Training Datasets
Xiangyu Sun, Raquel Aoki, Kevin H. Wilson
NotPlaNET: Removing False Positives from Planet Hunters TESS with Machine Learning
Valentina Tardugno Poleo, Nora Eisner, David W. Hogg
Unlocking Futures: A Natural Language Driven Career Prediction System for Computer Science and Software Engineering Students
Sakir Hossain Faruque, Sharun Akter Khushbu, Sharmin Akter
Is machine learning good or bad for the natural sciences?
David W. Hogg, Soledad Villar
Design Principles for Falsifiable, Replicable and Reproducible Empirical ML Research
Daniel Vranješ, Oliver Niggemann