Conditional Likelihood

Conditional likelihood focuses on estimating the probability of an event given specific conditions, a crucial task in various machine learning applications. Current research emphasizes its use in diverse areas, including link prediction in graphs (using diffusion models), membership inference attacks on large language models (via relative conditional log-likelihoods), and self-supervised domain adaptation in semantic segmentation (employing conditional maximum likelihood approaches). These advancements improve model robustness, generalization, and the ability to handle complex data structures, impacting fields ranging from network analysis and data security to image processing and predictive modeling.