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cs.LG2026
TabSieve: Explicit In-Table Evidence Selection for Tabular Prediction
Yongyao Wang, Ziqi Miao, Lu Yang +4
Tabular prediction can benefit from in-table rows as few-shot evidence, yet existing tabular models typically perform instance-wise inference and LLM-based prompting is often britt…
cs.LG2024
Augmenting Replay in World Models for Continual Reinforcement Learning
Luke Yang, Levin Kuhlmann, Gideon Kowadlo
Continual RL requires an agent to learn new tasks without forgetting previous ones, while improving on both past and future tasks. The most common approaches use model-free algorit…