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
Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning
Nina Horat, Sina Klerings, Sebastian Lerch
Verbalized Machine Learning: Revisiting Machine Learning with Language Models
Tim Z. Xiao, Robert Bamler, Bernhard Schölkopf, Weiyang Liu
xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology
Julius Hense, Mina Jamshidi Idaji, Oliver Eberle, Thomas Schnake, Jonas Dippel, Laure Ciernik, Oliver Buchstab, Andreas Mock, Frederick Klauschen, Klaus-Robert Müller
Data Measurements for Decentralized Data Markets
Charles Lu, Mohammad Mohammadi Amiri, Ramesh Raskar
Promoting Fairness and Diversity in Speech Datasets for Mental Health and Neurological Disorders Research
Eleonora Mancini, Ana Tanevska, Andrea Galassi, Alessio Galatolo, Federico Ruggeri, Paolo Torroni
Semmeldetector: Application of Machine Learning in Commercial Bakeries
Thomas H. Schmitt, Maximilian Bundscherer, Tobias Bocklet
Position: Embracing Negative Results in Machine Learning
Florian Karl, Lukas Malte Kemeter, Gabriel Dax, Paulina Sierak
A Comparison of Recent Algorithms for Symbolic Regression to Genetic Programming
Yousef A. Radwan, Gabriel Kronberger, Stephan Winkler
Explaining the Contributing Factors for Vulnerability Detection in Machine Learning
Esma Mouine, Yan Liu, Lu Xiao, Rick Kazman, Xiao Wang
Position: A Call to Action for a Human-Centered AutoML Paradigm
Marius Lindauer, Florian Karl, Anne Klier, Julia Moosbauer, Alexander Tornede, Andreas Mueller, Frank Hutter, Matthias Feurer, Bernd Bischl
Empowering Safe Reinforcement Learning for Power System Control with CommonPower
Michael Eichelbeck, Hannah Markgraf, Matthias Althoff