Estimation Problem

Estimation problems, central to numerous scientific fields, focus on accurately determining unknown parameters or values from available data, often under constraints like privacy limitations or noisy measurements. Current research emphasizes developing efficient and robust estimation algorithms, including those based on randomized smoothing, distributed optimization frameworks (like Estimation Network Design), and adaptations of Sinkhorn's algorithm for specific model types, addressing challenges posed by heterogeneous data, missing values, and adversarial attacks. These advancements improve the accuracy and efficiency of data analysis across diverse applications, from sensor networks and choice modeling to robust machine learning and distributed filtering.