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
On the Consistency of Fairness Measurement Methods for Regression Tasks
Abdalwahab Almajed, Maryam Tabar, Peyman Najafirad
ModSec-Learn: Boosting ModSecurity with Machine Learning
Christian Scano, Giuseppe Floris, Biagio Montaruli, Luca Demetrio, Andrea Valenza, Luca Compagna, Davide Ariu, Luca Piras, Davide Balzarotti, Battista Biggio
Unifying Mixed Gas Adsorption in Molecular Sieve Membranes and MOFs using Machine Learning
Subhadeep Dasgupta, Amal R S, Prabal K. Maiti
Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment
Julius von Kügelgen
Data Collection and Labeling Techniques for Machine Learning
Qianyu Huang, Tongfang Zhao
Enhancing supply chain security with automated machine learning
Haibo Wang, Lutfu S.Sua, Bahram Alidaee
Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning
Peter Eastman, Benjamin P. Pritchard, John D. Chodera, Thomas E. Markland
Machine Learning and Optimization Techniques for Solving Inverse Kinematics in a 7-DOF Robotic Arm
Enoch Adediran, Salem Ameen
Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation
Nikolas Koutsoubis, Yasin Yilmaz, Ravi P. Ramachandran, Matthew Schabath, Ghulam Rasool
Unsupervised explainable activity prediction in competitive Nordic Walking from experimental data
Silvia García-Méndez, Francisco de Arriba-Pérez, Francisco J. González-Castaño, Javier Vales-Alonso
GFM4MPM: Towards Geospatial Foundation Models for Mineral Prospectivity Mapping
Angel Daruna, Vasily Zadorozhnyy, Georgina Lukoczki, Han-Pang Chiu
Automatic generation of insights from workers' actions in industrial workflows with explainable Machine Learning
Francisco de Arriba-Pérez, Silvia García-Méndez, Javier Otero-Mosquera, Francisco J. González-Castaño, Felipe Gil-Castiñeira
TREE: Tree Regularization for Efficient Execution
Lena Schmid, Daniel Biebert, Christian Hakert, Kuan-Hsun Chen, Michel Lang, Markus Pauly, Jian-Jia Chen
Machine Learning Based Prediction of Proton Conductivity in Metal-Organic Frameworks
Seunghee Han, Byeong Gwan Lee, Dae Woon Lim, Jihan Kim
Research on Dangerous Flight Weather Prediction based on Machine Learning
Haoxing Liu, Renjie Xie, Haoshen Qin, Yizhou Li
Lexidate: Model Evaluation and Selection with Lexicase
Jose Guadalupe Hernandez, Anil Kumar Saini, Jason H. Moore
To Clip or not to Clip: the Dynamics of SGD with Gradient Clipping in High-Dimensions
Noah Marshall, Ke Liang Xiao, Atish Agarwala, Elliot Paquette
Understanding "Democratization" in NLP and ML Research
Arjun Subramonian, Vagrant Gautam, Dietrich Klakow, Zeerak Talat
Development of an Adaptive Multi-Domain Artificial Intelligence System Built using Machine Learning and Expert Systems Technologies
Jeremy Straub
Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning
Michał Dereziński, Michael W. Mahoney