Large Scale Real World

Research on large-scale real-world datasets focuses on developing and evaluating machine learning models under realistic, complex conditions, moving beyond idealized synthetic data. Current efforts concentrate on addressing challenges like logging policy confounding in ranking models, improving low-light image and video processing using event cameras, and enhancing the robustness of semi-supervised learning through techniques like prototype fission. This work is crucial for advancing the reliability and applicability of machine learning across diverse domains, from financial risk assessment to autonomous vehicle safety and beyond.