Over the last few decades, the integration of AI-driven computational techniques into digital pathology has revolutionized survival prediction tasks. However, most existing methods in survival analysis discretize the entire survival period into predefined intervals, overlooking the inherent uncertainty in event occurrence and the heterogeneity of patient survival times. The censored data further exacerbate these challenges, amplifying uncertainty and variability. To address these limitations, we introduce the Dirichlet distribution to model discretized outputs as continuous probability distributions, providing a more accurate representation of uncertainty awareness. Building upon this foundation, we propose a universal multi-modal survival analysis loss function that leverages uncertainty-driven fusion. Our Uncertainty-Aware Multi-Modal Survival Analysis (UMSA) framework further explores the interactions between multi-scale pathological images and genomic data, providing promising insights into multi-modal survival analysis. Experimental evaluations on five publicly available datasets demonstrate that UMSA achieves state-of-the-art performance, validating its effectiveness and scalability in survival prediction tasks.

Human-in-the-loop (HIL) imitation learning enables agents to learn complex behaviors safely through real-time human intervention. However, existing methods struggle to efficiently leverage agent-generated data due to dynamically evolving trajectory distributions and imperfections caused by human intervention delays, often failing to faithfully imitate the human expert policy. In this work, we propose Faithful Dynamic Imitation Learning (FaithDaIL) to address these challenges. We formulate HIL imitation learning as an online non-convex problem and employ dynamic regret minimization to adapt to the shifting data distribution and track high-quality policy trajectories. To ensure faithful imitation of the human expert despite training on mixed agent and human data, we introduce an unbiased imitation objective and achieve it by weighting the behavior distribution relative to the human expert's as a proxy reward. Extensive experiments on MetaDrive and CARLA driving benchmarks demonstrate that FaithDaIL achieves state-of-the-art performance in safety and task success with significantly reduced human intervention data compared to prior HIL baselines.