Faster Results from a Smarter Schedule: Reframing Collegiate Cross Country through Analysis of the National Running Club Database

A new method for addressing the inverse scattering problem has been introduced, leveraging a model-guided neural network approach. This technique aims to enhance the accuracy of imaging in fields such as medical imaging, remote sensing, and non-destructive testing by incorporating explicit physics into machine learning frameworks, which traditionally struggle with highly nonlinear scattering behaviors.

Recent advancements in large language models (LLMs) have led to the development of LLM4FS, a hybrid strategy that combines LLMs with traditional data-driven methods for automated feature selection. This approach evaluates state-of-the-art models like DeepSeek-R1 and GPT-4.5, demonstrating superior performance in selecting relevant features for decision-making tasks.

A new study has introduced nonparametric randomization tests for assessing conditional group symmetry, addressing a gap in statistical literature regarding tests for conditional invariance. This research develops a framework that ensures finite-sample Type I error control and implements tests using kernel methods, demonstrating their application in high-energy particle physics.

A recent study has explored the role of outer optimizers in Local Stochastic Gradient Descent (Local SGD), a method designed to enhance machine learning efficiency by minimizing communication overhead during training on large datasets. The research provides new convergence guarantees and emphasizes the importance of tuning the outer learning rate to improve model performance.

A recent study published on arXiv investigates the trade-off between the number of items ($N$) and the number of responses per item ($K$) in machine learning evaluations, emphasizing the importance of reproducibility and the impact of human disagreement in annotations. The research highlights that limited budgets for human-annotated data often lead to ignoring this disagreement, which can affect the reliability of evaluations.