The field of machine learning is moving towards more efficient and robust methods, with a focus on reducing computational complexity and improving model performance. One notable direction is the development of novel algorithms for selecting and prioritizing data points, enabling more effective on-device training and reducing storage requirements. Another area of advancement is the integration of geometric techniques, such as hyperspherical representations and hyperbolic space, to improve the accuracy and robustness of machine learning models. Additionally, researchers are exploring new methods for enforcing constraints and evaluating containment queries, with applications in graphics, engineering, and other fields. Noteworthy papers include: DRIP, which introduces a novel algorithm for online data point selection using Grad-CAM, and Towards Robust Trajectory Embedding, which proposes a hyperbolic space-based approach for trajectory representation learning. Constrained Machine Learning Through Hyperspherical Representation is also notable for its method to enforce constraints in output space, and Robust Containment Queries over Collections of Trimmed NURBS Surfaces via Generalized Winding Numbers presents a robust method for evaluating containment queries.