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
$C^*$-Algebraic Machine Learning: Moving in a New Direction
Yuka Hashimoto, Masahiro Ikeda, Hachem Kadri
Nature-Inspired Local Propagation
Alessandro Betti, Marco Gori
Early stopping by correlating online indicators in neural networks
Manuel Vilares Ferro, Yerai Doval Mosquera, Francisco J. Ribadas Pena, Victor M. Darriba Bilbao
Review of multimodal machine learning approaches in healthcare
Felix Krones, Umar Marikkar, Guy Parsons, Adam Szmul, Adam Mahdi
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains
Mustafa Hajij, Mathilde Papillon, Florian Frantzen, Jens Agerberg, Ibrahem AlJabea, Ruben Ballester, Claudio Battiloro, Guillermo Bernárdez, Tolga Birdal, Aiden Brent, Peter Chin, Sergio Escalera, Simone Fiorellino, Odin Hoff Gardaa, Gurusankar Gopalakrishnan, Devendra Govil, Josef Hoppe, Maneel Reddy Karri, Jude Khouja, Manuel Lecha, Neal Livesay, Jan Meißner, Soham Mukherjee, Alexander Nikitin, Theodore Papamarkou, Jaro Prílepok, Karthikeyan Natesan Ramamurthy, Paul Rosen, Aldo Guzmán-Sáenz, Alessandro Salatiello, Shreyas N. Samaga, Simone Scardapane, Michael T. Schaub, Luca Scofano, Indro Spinelli, Lev Telyatnikov, Quang Truong, Robin Walters, Maosheng Yang, Olga Zaghen, Ghada Zamzmi, Ali Zia, Nina Miolane
Machine Intelligence in Africa: a survey
Allahsera Auguste Tapo, Ali Traore, Sidy Danioko, Hamidou Tembine
Online Transfer Learning for RSV Case Detection
Yiming Sun, Yuhe Gao, Runxue Bao, Gregory F. Cooper, Jessi Espino, Harry Hochheiser, Marian G. Michaels, John M. Aronis, Chenxi Song, Ye Ye
Simulation-Enhanced Data Augmentation for Machine Learning Pathloss Prediction
Ahmed P. Mohamed, Byunghyun Lee, Yaguang Zhang, Max Hollingsworth, C. Robert Anderson, James V. Krogmeier, David J. Love
Robust Counterfactual Explanations in Machine Learning: A Survey
Junqi Jiang, Francesco Leofante, Antonio Rago, Francesca Toni
On Catastrophic Inheritance of Large Foundation Models
Hao Chen, Bhiksha Raj, Xing Xie, Jindong Wang
On $f$-Divergence Principled Domain Adaptation: An Improved Framework
Ziqiao Wang, Yongyi Mao
Large Language Model Agent for Hyper-Parameter Optimization
Siyi Liu, Chen Gao, Yong Li
Mission Critical -- Satellite Data is a Distinct Modality in Machine Learning
Esther Rolf, Konstantin Klemmer, Caleb Robinson, Hannah Kerner
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)
Zeliang Kan, Shae McFadden, Daniel Arp, Feargus Pendlebury, Roberto Jordaney, Johannes Kinder, Fabio Pierazzi, Lorenzo Cavallaro
Comparative Evaluation of Weather Forecasting using Machine Learning Models
Md Saydur Rahman, Farhana Akter Tumpa, Md Shazid Islam, Abul Al Arabi, Md Sanzid Bin Hossain, Md Saad Ul Haque
Salsa Fresca: Angular Embeddings and Pre-Training for ML Attacks on Learning With Errors
Samuel Stevens, Emily Wenger, Cathy Li, Niklas Nolte, Eshika Saxena, François Charton, Kristin Lauter