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
Pragmatic auditing: a pilot-driven approach for auditing Machine Learning systems
Djalel Benbouzid, Christiane Plociennik, Laura Lucaj, Mihai Maftei, Iris Merget, Aljoscha Burchardt, Marc P. Hauer, Abdeldjallil Naceri, Patrick van der Smagt
A novel reliability attack of Physical Unclonable Functions
Gaoxiang Li, Yu Zhuang
Dynamic Line Rating using Hyper-local Weather Predictions: A Machine Learning Approach
Henri Manninen, Markus Lippus, Georg Rute
Channel Balance Interpolation in the Lightning Network via Machine Learning
Vincent, Emanuele Rossi, Vikash Singh
Naming the Pain in Machine Learning-Enabled Systems Engineering
Marcos Kalinowski, Daniel Mendez, Görkem Giray, Antonio Pedro Santos Alves, Kelly Azevedo, Tatiana Escovedo, Hugo Villamizar, Helio Lopes, Teresa Baldassarre, Stefan Wagner, Stefan Biffl, Jürgen Musil, Michael Felderer, Niklas Lavesson, Tony Gorschek
A Three-Phase Analysis of Synergistic Effects During Co-pyrolysis of Algae and Wood for Biochar Yield Using Machine Learning
Subhadeep Chakrabarti, Saish Shinde
Interpretability of Statistical, Machine Learning, and Deep Learning Models for Landslide Susceptibility Mapping in Three Gorges Reservoir Area
Cheng Chen, Lei Fan
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