Conditional Stochastic Optimization

Conditional stochastic optimization (CSO) focuses on solving optimization problems where the objective function involves conditional expectations, posing challenges due to inherent bias in sample-based estimations. Current research emphasizes developing efficient algorithms, such as multi-level Monte Carlo methods and variance reduction techniques, to mitigate this bias and improve convergence rates, particularly for nonconvex problems. These advancements are impacting diverse fields, including machine learning (e.g., federated learning, contrastive learning), causal inference, and material science, by enabling more efficient and accurate solutions to complex optimization tasks involving uncertainty and conditional dependencies.