Large Scale Learning

Large-scale learning tackles the challenges of training machine learning models on massive datasets and complex architectures. Current research focuses on developing efficient algorithms (like stochastic gradient descent variants and distributed optimization methods), novel model architectures (including transformers, Kolmogorov-Arnold Networks, and sparse models), and techniques to reduce computational costs (e.g., model pruning, safe screening, and compressive learning). These advancements are crucial for addressing real-world problems across diverse fields, from medical image analysis and natural language processing to fluid dynamics simulation and exoplanet detection, by enabling the training of more accurate and robust models on previously intractable datasets.