Joint Inference

Joint inference, a rapidly developing field, focuses on simultaneously estimating multiple related variables or parameters from data, offering more efficient and robust solutions compared to sequential estimation. Current research emphasizes developing scalable algorithms, such as those based on variational inference, alternating least squares, and generative flow networks, to handle high-dimensional data and complex model architectures, including Bayesian networks and deep equilibrium models. This approach is proving valuable across diverse applications, from improving the efficiency of personalized machine learning on resource-constrained devices to enabling privacy-preserving collaborative learning in federated settings and advancing causal inference in high-dimensional data.