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
Robust Counterfactual Explanations in Machine Learning: A Survey
Junqi Jiang, Francesco Leofante, Antonio Rago, Francesca Toni
SMLP: Symbolic Machine Learning Prover
Franz Brauße, Zurab Khasidashvili, Konstantin Korovin
Supervised Algorithmic Fairness in Distribution Shifts: A Survey
Minglai Shao, Dong Li, Chen Zhao, Xintao Wu, Yujie Lin, Qin Tian
Comparative Evaluation of Weather Forecasting using Machine Learning Models
Md Saydur Rahman, Farhana Akter Tumpa, Md Shazid Islam, Abul Al Arabi, Md Sanzid Bin Hossain, Md Saad Ul Haque
Machine Learning Modeling Of SiRNA Structure-Potency Relationship With Applications Against Sars-Cov-2 Spike Gene
Damilola Oshunyinka
On the Readiness of Scientific Data for a Fair and Transparent Use in Machine Learning
Joan Giner-Miguelez, Abel Gómez, Jordi Cabot
Explaining Drift using Shapley Values
Narayanan U. Edakunni, Utkarsh Tekriwal, Anukriti Jain