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.
3418papers
Papers - Page 55
May 31, 2024
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 HallerClass-Based Time Series Data Augmentation to Mitigate Extreme Class Imbalance for Solar Flare Prediction
Junzhi Wen, Rafal A. AngrykA Novel Review of Stability Techniques for Improved Privacy-Preserving Machine Learning
Coleman DuPlessie, Aidan Gao
May 30, 2024
Policy Trees for Prediction: Interpretable and Adaptive Model Selection for Machine Learning
Dimitris Bertsimas, Matthew PeroniScaling Laws for the Value of Individual Data Points in Machine Learning
Ian Covert, Wenlong Ji, Tatsunori Hashimoto, James ZouUnderstanding Encoder-Decoder Structures in Machine Learning Using Information Measures
Jorge F. Silva, Victor Faraggi, Camilo Ramirez, Alvaro Egana, Eduardo PavezThe 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 NaqviFederated and Transfer Learning for Cancer Detection Based on Image Analysis
Amine Bechar, Youssef Elmir, Yassine Himeur, Rafik Medjoudj, Abbes AmiraA 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 ZhouTask-Agnostic Machine-Learning-Assisted Inference
Jiacheng Miao, Qiongshi Lu
May 29, 2024
May 28, 2024
Counterfactual Explanations for Multivariate Time-Series without Training Datasets
Xiangyu Sun, Raquel Aoki, Kevin H. WilsonNotPlaNET: Removing False Positives from Planet Hunters TESS with Machine Learning
Valentina Tardugno Poleo, Nora Eisner, David W. HoggUnlocking Futures: A Natural Language Driven Career Prediction System for Computer Science and Software Engineering Students
Sakir Hossain Faruque, Sharun Akter Khushbu, Sharmin AkterIs machine learning good or bad for the natural sciences?
David W. Hogg, Soledad VillarDesign Principles for Falsifiable, Replicable and Reproducible Empirical ML Research
Daniel Vranješ, Oliver Niggemann