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
The Thousand Faces of Explainable AI Along the Machine Learning Life Cycle: Industrial Reality and Current State of Research
Thomas Decker, Ralf Gross, Alexander Koebler, Michael Lebacher, Ronald Schnitzer, Stefan H. Weber
Energy Estimates Across Layers of Computing: From Devices to Large-Scale Applications in Machine Learning for Natural Language Processing, Scientific Computing, and Cryptocurrency Mining
Sadasivan Shankar
Histopathological Image Classification and Vulnerability Analysis using Federated Learning
Sankalp Vyas, Amar Nath Patra, Raj Mani Shukla
Machine Learning Methods for Background Potential Estimation in 2DEGs
Carlo da Cunha, Nobuyuki Aoki, David Ferry, Kevin Vora, Yu Zhang
Advancing Diagnostic Precision: Leveraging Machine Learning Techniques for Accurate Detection of Covid-19, Pneumonia, and Tuberculosis in Chest X-Ray Images
Aditya Kulkarni, Guruprasad Parasnis, Harish Balasubramanian, Vansh Jain, Anmol Chokshi, Reena Sonkusare
A New Transformation Approach for Uplift Modeling with Binary Outcome
Kun Li, Jiang Tian, Xiaojia Xiang
Cost-Sensitive Best Subset Selection for Logistic Regression: A Mixed-Integer Conic Optimization Perspective
Ricardo Knauer, Erik Rodner
Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology
Qingsong Xu, Yilei Shi, Jonathan Bamber, Ye Tuo, Ralf Ludwig, Xiao Xiang Zhu
Human-in-the-loop: The future of Machine Learning in Automated Electron Microscopy
Sergei V. Kalinin, Yongtao Liu, Arpan Biswas, Gerd Duscher, Utkarsh Pratiush, Kevin Roccapriore, Maxim Ziatdinov, Rama Vasudevan
Interventions Against Machine-Assisted Statistical Discrimination
John Y. Zhu
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Dominique Beaini, Shenyang Huang, Joao Alex Cunha, Zhiyi Li, Gabriela Moisescu-Pareja, Oleksandr Dymov, Samuel Maddrell-Mander, Callum McLean, Frederik Wenkel, Luis Müller, Jama Hussein Mohamud, Ali Parviz, Michael Craig, Michał Koziarski, Jiarui Lu, Zhaocheng Zhu, Cristian Gabellini, Kerstin Klaser, Josef Dean, Cas Wognum, Maciej Sypetkowski, Guillaume Rabusseau, Reihaneh Rabbany, Jian Tang, Christopher Morris, Ioannis Koutis, Mirco Ravanelli, Guy Wolf, Prudencio Tossou, Hadrien Mary, Therence Bois, Andrew Fitzgibbon, Błażej Banaszewski, Chad Martin, Dominic Masters
Bringing Quantum Algorithms to Automated Machine Learning: A Systematic Review of AutoML Frameworks Regarding Extensibility for QML Algorithms
Dennis Klau, Marc Zöller, Christian Tutschku
Cost-Effective Retraining of Machine Learning Models
Ananth Mahadevan, Michael Mathioudakis
Genetic prediction of quantitative traits: a machine learner's guide focused on height
Lucie Bourguignon, Caroline Weis, Catherine R. Jutzeler, Michael Adamer
CLASSify: A Web-Based Tool for Machine Learning
Aaron D. Mullen, Samuel E. Armstrong, Jeff Talbert, V. K. Cody Bumgardner
MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation
Qian Huang, Jian Vora, Percy Liang, Jure Leskovec
Formal and Practical Elements for the Certification of Machine Learning Systems
Jean-Guillaume Durand, Arthur Dubois, Robert J. Moss