Machine Learning Model
Machine learning models aim to create systems that can learn from data and make predictions or decisions without explicit programming. Current research emphasizes improving model accuracy, interpretability, and robustness, focusing on architectures like deep neural networks, decision tree ensembles, and transformer models, as well as exploring decentralized learning and techniques for mitigating biases and vulnerabilities. These advancements are crucial for diverse applications, ranging from optimizing resource management (e.g., smart irrigation) to improving healthcare diagnostics and enhancing the security and trustworthiness of AI systems.
Papers
Navigating Ensemble Configurations for Algorithmic Fairness
Michael Feffer, Martin Hirzel, Samuel C. Hoffman, Kiran Kate, Parikshit Ram, Avraham Shinnar
Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data
Meghdad Kurmanji, Peter Triantafillou
Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning
Alex J. Chan, Mihaela van der Schaar
New Metric Formulas that Include Measurement Errors in Machine Learning for Natural Sciences
Umberto Michelucci, Francesca Venturini
Fault Prognosis in Particle Accelerator Power Electronics Using Ensemble Learning
Majdi I. Radaideh, Chris Pappas, Mark Wezensky, Pradeep Ramuhalli, Sarah Cousineau
Rethinking and Recomputing the Value of ML Models
Burcu Sayin, Fabio Casati, Andrea Passerini, Jie Yang, Xinyue Chen
Machine Beats Machine: Machine Learning Models to Defend Against Adversarial Attacks
Jože M. Rožanec, Dimitrios Papamartzivanos, Entso Veliou, Theodora Anastasiou, Jelle Keizer, Blaž Fortuna, Dunja Mladenić
Forecasting Sensor Values in Waste-To-Fuel Plants: a Case Study
Bor Brecelj, Beno Šircelj, Jože M. Rožanec, Blaž Fortuna, Dunja Mladenić
MLink: Linking Black-Box Models from Multiple Domains for Collaborative Inference
Mu Yuan, Lan Zhang, Zimu Zheng, Yi-Nan Zhang, Xiang-Yang Li