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
AI Guided Early Screening of Cervical Cancer
Dharanidharan S I, Suhitha Renuka S V, Ajishi Singh, Sheena Christabel Pravin
Non-IID data in Federated Learning: A Systematic Review with Taxonomy, Metrics, Methods, Frameworks and Future Directions
Daniel M. Jimenez G., David Solans, Mikko Heikkila, Andrea Vitaletti, Nicolas Kourtellis, Aris Anagnostopoulos, Ioannis Chatzigiannakis
A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning
Caleb J.S. Barr, Olivia Erdelyi, Paul D. Docherty, Randolph C. Grace
Fitting Multiple Machine Learning Models with Performance Based Clustering
Mehmet Efe Lorasdagi, Ahmet Berker Koc, Ali Taha Koc, Suleyman Serdar Kozat
ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?
Canyu Chen, Jian Yu, Shan Chen, Che Liu, Zhongwei Wan, Danielle Bitterman, Fei Wang, Kai Shu
Neuro-Symbolic Rule Lists
Sascha Xu, Nils Philipp Walter, Jilles Vreeken
Oblivious Defense in ML Models: Backdoor Removal without Detection
Shafi Goldwasser, Jonathan Shafer, Neekon Vafa, Vinod Vaikuntanathan
Evaluating Machine Learning Models against Clinical Protocols for Enhanced Interpretability and Continuity of Care
Christel Sirocchi, Muhammad Suffian, Federico Sabbatini, Alessandro Bogliolo, Sara Montagna
A Bayesian explanation of machine learning models based on modes and functional ANOVA
Quan Long
An information-matching approach to optimal experimental design and active learning
Yonatan Kurniawan (1), Tracianne B. Neilsen (1), Benjamin L. Francis (2), Alex M. Stankovic (3), Mingjian Wen (4), Ilia Nikiforov (5), Ellad B. Tadmor (5), Vasily V. Bulatov (6), Vincenzo Lordi (6), Mark K. Transtrum (1, 2, and 3) ((1) Brigham Young University, Provo, UT, USA, (2) Achilles Heel Technologies, Orem, UT, USA, (3) SLAC National Accelerator Laboratory, Menlo Park, CA, USA, (4) University of Houston, Houston, TX, USA, (5) University of Minnesota, Minneapolis, MN, USA, (6) Lawrence Livermore National Laboratory)