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
Composite Bayesian Optimization In Function Spaces Using NEON -- Neural Epistemic Operator Networks
Leonardo Ferreira Guilhoto, Paris Perdikaris
Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference
Fred Hohman, Chaoqun Wang, Jinmook Lee, Jochen Görtler, Dominik Moritz, Jeffrey P Bigham, Zhile Ren, Cecile Foret, Qi Shan, Xiaoyi Zhang
ML2SC: Deploying Machine Learning Models as Smart Contracts on the Blockchain
Zhikai Li, Steve Vott, Bhaskar Krishnamachar
Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences
Dimitris Bertsimas, Vassilis Digalakis, Yu Ma, Phevos Paschalidis
Evaluating Explanatory Capabilities of Machine Learning Models in Medical Diagnostics: A Human-in-the-Loop Approach
José Bobes-Bascarán, Eduardo Mosqueira-Rey, Ángel Fernández-Leal, Elena Hernández-Pereira, David Alonso-Ríos, Vicente Moret-Bonillo, Israel Figueirido-Arnoso, Yolanda Vidal-Ínsua
Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions
Zainab Al-Zanbouri, Gauri Sharma, Shaina Raza
First Experiences with the Identification of People at Risk for Diabetes in Argentina using Machine Learning Techniques
Enzo Rucci, Gonzalo Tittarelli, Franco Ronchetti, Jorge F. Elgart, Laura Lanzarini, Juan José Gagliardino