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
How to integrate cloud service, data analytic and machine learning technique to reduce cyber risks associated with the modern cloud based infrastructure
Upakar Bhatta
Machine Learning & Wi-Fi: Unveiling the Path Towards AI/ML-Native IEEE 802.11 Networks
Francesc Wilhelmi, Szymon Szott, Katarzyna Kosek-Szott, Boris Bellalta
Leveraging Machine Learning for Accurate IoT Device Identification in Dynamic Wireless Contexts
Bhagyashri Tushir, Vikram K Ramanna, Yuhong Liu, Behnam Dezfouli
Sensing technologies and machine learning methods for emotion recognition in autism: Systematic review
Oresti Banos, Zhoe Comas-González, Javier Medina, Aurora Polo-Rodríguez, David Gil, Jesús Peral, Sandra Amador, Claudia Villalonga
Fair Generalized Linear Mixed Models
Jan Pablo Burgard, João Vitor Pamplona
Enhancing Airline Customer Satisfaction: A Machine Learning and Causal Analysis Approach
Tejas Mirthipati
What is it for a Machine Learning Model to Have a Capability?
Jacqueline Harding, Nathaniel Sharadin
A Brief Introduction to Causal Inference in Machine Learning
Kyunghyun Cho
Optimal design of experiments in the context of machine-learning inter-atomic potentials: improving the efficiency and transferability of kernel based methods
Bartosz Barzdajn, Christopher P. Race
Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation
Yacine Izza, Xuanxiang Huang, Antonio Morgado, Jordi Planes, Alexey Ignatiev, Joao Marques-Silva
Learning functions on symmetric matrices and point clouds via lightweight invariant features
Ben Blum-Smith, Ningyuan Huang, Marco Cuturi, Soledad Villar
Sample Selection Bias in Machine Learning for Healthcare
Vinod Kumar Chauhan, Lei Clifton, Achille Salaün, Huiqi Yvonne Lu, Kim Branson, Patrick Schwab, Gaurav Nigam, David A. Clifton
Integrating Multi-Physics Simulations and Machine Learning to Define the Spatter Mechanism and Process Window in Laser Powder Bed Fusion
Olabode T. Ajenifujah, Francis Ogoke, Florian Wirth, Jack Beuth, Amir Barati Farimani
Challenges and Opportunities of NLP for HR Applications: A Discussion Paper
Jochen L. Leidner, Mark Stevenson
DeepFMEA -- A Scalable Framework Harmonizing Process Expertise and Data-Driven PHM
Christoph Netsch, Till Schöpe, Benedikt Schindele, Joyam Jayakumar