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
Advanced LSTM Neural Networks for Predicting Directional Changes in Sector-Specific ETFs Using Machine Learning Techniques
Rifa Gowani, Zaryab Kanjiani
3D-SAR Tomography and Machine Learning for High-Resolution Tree Height Estimation
Grace Colverd, Jumpei Takami, Laura Schade, Karol Bot, Joseph A. Gallego-Mejia
Normalizing Energy Consumption for Hardware-Independent Evaluation
Constance Douwes, Romain Serizel
Interpolation, Extrapolation, Hyperpolation: Generalising into new dimensions
Toby Ord
Advancing Machine Learning for Stellar Activity and Exoplanet Period Rotation
Fatemeh Fazel Hesar, Bernard Foing, Ana M. Heras, Mojtaba Raouf, Victoria Foing, Shima Javanmardi, Fons J. Verbeek
FedBrain-Distill: Communication-Efficient Federated Brain Tumor Classification Using Ensemble Knowledge Distillation on Non-IID Data
Rasoul Jafari Gohari, Laya Aliahmadipour, Ezat Valipour
Towards Automated Machine Learning Research
Shervin Ardeshir
CubicML: Automated ML for Large ML Systems Co-design with ML Prediction of Performance
Wei Wen, Quanyu Zhu, Weiwei Chu, Wen-Yen Chen, Jiyan Yang
Leveraging Machine Learning for Official Statistics: A Statistical Manifesto
Marco Puts, David Salgado, Piet Daas
Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression Labels
Emma Svensson, Hannah Rosa Friesacher, Susanne Winiwarter, Lewis Mervin, Adam Arany, Ola Engkvist
Understanding Data Importance in Machine Learning Attacks: Does Valuable Data Pose Greater Harm?
Rui Wen, Michael Backes, Yang Zhang
Threat Classification on Deployed Optical Networks Using MIMO Digital Fiber Sensing, Wavelets, and Machine Learning
Khouloud Abdelli, Henrique Pavani, Christian Dorize, Sterenn Guerrier, Haik Mardoyan, Patricia Layec, Jeremie Renaudier
Beyond Model Interpretability: Socio-Structural Explanations in Machine Learning
Andrew Smart, Atoosa Kasirzadeh
Efficient Multi-Task Large Model Training via Data Heterogeneity-aware Model Management
Yujie Wang, Shenhan Zhu, Fangcheng Fu, Xupeng Miao, Jie Zhang, Juan Zhu, Fan Hong, Yong Li, Bin Cui
Pricing American Options using Machine Learning Algorithms
Prudence Djagba, Callixte Ndizihiwe
Addressing the Gaps in Early Dementia Detection: A Path Towards Enhanced Diagnostic Models through Machine Learning
Juan A. Berrios Moya