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This paper introduces PopFS, a method for learning a shared feature set that is robust across heterogeneous populations, allowing each population to train its own model. Experiments on diverse tabular datasets and a 43-state COVID-19 nowcasting study show strong average and worst-population performance.
This paper argues that representation learning, not model-based planning, is the key to scalable multitask deep reinforcement learning. It introduces MR.Q, a simple model-free algorithm with auxiliary predictive objectives that outperforms prior world-model-based methods across diverse continuous control tasks.
TabPFN-MT extends PFNs to multitask in-context learning for tabular data, achieving state-of-the-art on small-to-medium datasets while reducing inference cost from O(T) to O(1) forward passes.