Full (exact) conformal set vs. split or cross-validated conformal set Non-connectedness of the conformal prediction set. This was initially suggested in [18, Remark 1]. We follow the actual practice in the literature [14, Remark 5]. We did not observe violations. We will also summarize the proposed algorithm in a direct pseudo-code.

We thank the reviewers for their thoughtful and constructive feedback on our manuscript. This should help both contextualize each task's difficulty and illustrate what it involves. Reviewer 3 noted the Section 2 task descriptions could be better presented. We have reformatted it so that "the order We also changed our description of IMP ALA to match Reviewer 5's suggestion. Regarding the task suite, Reviewer 4 raised a thoughtful consideration on whether "most of the findings translate when Some 3D tasks in the suite already have '2D-like' semi-counterparts that do not require navigation, '2D-like' because everything is fully observable and the agent has a first-person point of view from a fixed point, without Spot the Difference level, was overall harder than Change Detection for our ablation models.

Y es, the total complexity is proportional to the number of aggregated tasks. Add experiments to compare ANIL and MAML and w.r .t. the size B of samples: Why sample size in inner-loop is not taken into analysis, as Fallah et al. [4] does: This setting has also been considered in Rajeswaran et al. [24], Ji et al. [13]. Reviewer 2: Dependence on κ. iMAML depends on κ in contrast to poly (κ) of this work: Add an experiment to verify the tightness: Great point! W e will definitely add such an experiment in the revision. W e will clarify it in the revision.

The level of autonomy is increasing in systems spanning multiple domains, but these systems still experience failures. One way to mitigate the risk of failures is to integrate human oversight of the autonomous systems and rely on the human to take control when the autonomy fails. In this work, we formulate a method of collaborative decision making through action suggestions that improves action selection without taking control of the system. Our approach uses each suggestion efficiently by incorporating the implicit information shared through suggestions to modify the agent's belief and achieves better performance with fewer suggestions than naively following the suggested actions. We assume collaborative agents share the same objective and communicate through valid actions. By assuming the suggested action is dependent only on the state, we can incorporate the suggested action as an independent observation of the environment. The assumption of a collaborative environment enables us to use the agent's policy to estimate the distribution over action suggestions. We propose two methods that use suggested actions and demonstrate the approach through simulated experiments. The proposed methodology results in increased performance while also being robust to suboptimal suggestions.

Even after TikTok removed swastika jewelry from its online shop, I was algorithmically nudged toward a web of Nazi-related products during searches, like "double lightning bolt" and "ss" necklaces. My journey on TikTok Shop started out with a search for "hip hop jewelry." It's an innocuous search query multiple users have likely typed in, hoping to find something to wear. While browsing the cheap jewelry, I was struck by what TikTok's algorithm repeatedly suggested that I might also be interested in: jewelry with blatant Nazi symbolism. TikTok continues to struggle with moderation as its in-app ecommerce store gains traction with younger users.