Appendix for "Episodic Multi-T ask Learning with Heterogeneous Neural Processes" In this section, we list frequently asked questions from researchers who help proofread this manuscript. As shown in Table 1, we use "Heterogeneous tasks" to distinguish the different branches of multi-task Meanwhile, "Episodic training" is used to describe the data-feeding strategy. Thus, "Heterogeneous tasks" is not available here (-). In episodic multi-task learning, we restrict the scope of the problem to the case where tasks in the same episode are related and share the same target space. This also implies that tasks with the same target space are related.

The proofs of Lemmas A.2 and A.3 follow from covering arguments and Bernstein's inequality; refer

We conducted the experiment on Meta-World, a multi-task RL benchmark containing various robotics manipulation tasks.

Even in large language models, negative transfer problems may still exist.

Even in large language models, negative transfer problems may still exist.