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
Machine-learning prediction of tipping with applications to the Atlantic Meridional Overturning Circulation
Shirin Panahi, Ling-Wei Kong, Mohammadamin Moradi, Zheng-Meng Zhai, Bryan Glaz, Mulugeta Haile, Ying-Cheng Lai
Stable Update of Regression Trees
Morten Blørstad, Berent Å. S. Lunde, Nello Blaser
The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review
Daniel Schwabe, Katinka Becker, Martin Seyferth, Andreas Klaß, Tobias Schäffter
Data-driven Discovery with Large Generative Models
Bodhisattwa Prasad Majumder, Harshit Surana, Dhruv Agarwal, Sanchaita Hazra, Ashish Sabharwal, Peter Clark
Harnessing Large Language Models as Post-hoc Correctors
Zhiqiang Zhong, Kuangyu Zhou, Davide Mottin
Rigor with Machine Learning from Field Theory to the Poincar\'e Conjecture
Sergei Gukov, James Halverson, Fabian Ruehle
Toward Fairness via Maximum Mean Discrepancy Regularization on Logits Space
Hao-Wei Chung, Ching-Hao Chiu, Yu-Jen Chen, Yiyu Shi, Tsung-Yi Ho
Spurious Correlations in Machine Learning: A Survey
Wenqian Ye, Guangtao Zheng, Xu Cao, Yunsheng Ma, Aidong Zhang
A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective
Jeng-Lin Li, Chih-Fan Hsu, Ming-Ching Chang, Wei-Chao Chen
Evaluation of Country Dietary Habits Using Machine Learning Techniques in Relation to Deaths from COVID-19
María Teresa García-Ordás, Natalia Arias, Carmen Benavides, Oscar García-Olalla, José Alberto Benítez-Andrades
Grounding from an AI and Cognitive Science Lens
Goonmeet Bajaj, Srinivasan Parthasarathy, Valerie L. Shalin, Amit Sheth
The Fundamental Limits of Least-Privilege Learning
Theresa Stadler, Bogdan Kulynych, Michael C. Gastpar, Nicolas Papernot, Carmela Troncoso
Explaining the Machine Learning Solution of the Ising Model
Roberto C. Alamino
Image Denoising with Machine Learning: A Novel Approach to Improve Quantum Image Processing Quality and Reliability
Yifan Zhou, Yan Shing Liang
Improved Indoor Localization with Machine Learning Techniques for IoT applications
M. W. P. Maduranga