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High-dimensional mediation analysis is often associated with a multiple testing problem for detecting significant mediators. Assessing the uncertainty of this detecting process via false discovery rate (FDR) has garnered great interest. To control the FDR in multiple testing, two essential steps are involved: ranking and selection. Existing approaches either construct p-values without calibration or disregard the joint information across tests, leading to conservation in FDR control or non-optimal ranking rules for multiple hypotheses. In this paper, we develop an adaptive mediation detection procedure (referred to as "AMDP") to identify relevant mediators while asymptotically controlling the FDR in high-dimensional mediation analysis. AMDP produces the optimal rule for ranking hypotheses and proposes a data-driven strategy to determine the threshold for mediator selection. This novel method captures information from the proportions of composite null hypotheses and the distribution of p-values, which turns the high dimensionality into an advantage instead of a limitation. The numerical studies on synthetic and real data sets illustrate the performances of AMDP compared with existing approaches.

Should we care whether AI systems have representations of the world that are similar to those of humans? We provide an information-theoretic analysis that suggests that there should be a U-shaped relationship between the degree of representational alignment with humans and performance on few-shot learning tasks. We confirm this prediction empirically, finding such a relationship in an analysis of the performance of 491 computer vision models. We also show that highly-aligned models are more robust to both natural adversarial attacks and domain shifts. Our results suggest that human alignment is often a sufficient, but not necessary, condition for models to make effective use of limited data, be robust, and generalize well.

Based on the formulation of the likelihood function pΘ(G|I) = fΘ(G,I)/ZΘ(I), we can reformulate the gradient of log-likelihood function as: ΘL(Θ) = EG pd[ Θ log fΘ(G,I)] Θ log ZΘ(I). Theorem 2. In the initialization phase, the potential function ψtriplet(r,yoh,yot) for modeling label dependency is omitted in p(G|I), yielding a simplified model distribution ˆp(G|I). Now, we can exactly derive that q(G) = ˆp(G|I). Theorem 3. In the update phase, we use the full expression of p(G|I) with the potential function ψtriplet(r,yoh,yot) for modeling label dependency. In this case, maximizing L(q) is equivalent to minimizing the KL divergence term, and the minimum occurs when q(yo) = p(yo,I).

Few-shot knowledge graph (KG) completion task aims to perform inductive reasoning over the KG: given only a few support triplets of a new relation ./(e.g., (chop, ./,

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/