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
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
Decoding Cognitive Health Using Machine Learning: A Comprehensive Evaluation for Diagnosis of Significant Memory Concern
M. Sajid, Rahul Sharma, Iman Beheshti, M. Tanveer
Deciphering public attention to geoengineering and climate issues using machine learning and dynamic analysis
Ramit Debnath, Pengyu Zhang, Tianzhu Qin, R. Michael Alvarez, Shaun D. Fitzgerald
Reimplementation of Learning to Reweight Examples for Robust Deep Learning
Parth Patil, Ben Boardley, Jack Gardner, Emily Loiselle, Deerajkumar Parthipan
Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare
Xingyu Li, Lu Peng, Yuping Wang, Weihua Zhang
The Role of Learning Algorithms in Collective Action
Omri Ben-Dov, Jake Fawkes, Samira Samadi, Amartya Sanyal
A First Step in Using Machine Learning Methods to Enhance Interaction Analysis for Embodied Learning Environments
Joyce Fonteles, Eduardo Davalos, Ashwin T. S., Yike Zhang, Mengxi Zhou, Efrat Ayalon, Alicia Lane, Selena Steinberg, Gabriella Anton, Joshua Danish, Noel Enyedy, Gautam Biswas
Detecting Moving Objects With Machine Learning
Wesley C. Fraser
LLMs for XAI: Future Directions for Explaining Explanations
Alexandra Zytek, Sara Pidò, Kalyan Veeramachaneni
Discovering hidden physics using ML-based multimodal super-resolution measurement and its application to fusion plasmas
Azarakhsh Jalalvand, SangKyeun Kim, Jaemin Seo, Qiming Hu, Max Curie, Peter Steiner, Andrew Oakleigh Nelson, Yong-Su Na, Egemen Kolemen
Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systems
Amin Aminifar, Matin Shokri, Amir Aminifar
Precision Rehabilitation for Patients Post-Stroke based on Electronic Health Records and Machine Learning
Fengyi Gao, Xingyu Zhang, Sonish Sivarajkumar, Parker Denny, Bayan Aldhahwani, Shyam Visweswaran, Ryan Shi, William Hogan, Allyn Bove, Yanshan Wang
Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency
Yuqi Zhou, Hao Zhu