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There are many applications where positive instances are rare but important to identify. For example, in NLP, positive sentences for a given relation are rare in a large corpus. Positive data are more informative for learning in these applications, but before one labels a certain amount of data, it is unknown where to find the rare positives. Since random sampling can lead to significant waste in labeling effort, previous ”active search” methods use a single bandit model to learn about the data distribution (exploration) while sampling from the regions potentially containing more positives (exploitation). Many bandit models are possible and a sub-optimal model reduces labeling efficiency, but the optimal model is unknown before any data are labeled. We propose Meta-AS (Meta Active Search) that uses a meta-bandit to evaluate a set of base bandits and aims to label positive examples efficiently, comparing to the optimal base bandit with hindsight. The meta-bandit estimates the mean and variance of the performance of the base bandits, and selects a base bandit to propose what data to label next for exploration or exploitation. The feedback in the labels updates both the base bandits and the meta-bandit for the next round. Meta-AS can accommodate a diverse set of base bandits to explore assumptions about the dataset, without over-committing to a single model before labeling starts. Experiments on five datasets for relation extraction demonstrate that Meta-AS labels positives more efficiently than the base bandits and other bandit selection strategies. 

Oxygen evolution reaction (OER) is of great significance for hydrogen production via water electrolysis, which, however, demands development of highly active, durable, and cost‐effective electrocatalysts in order to stride into a renewable energy era. Herein, highly efficient and long‐term durable OER by coupling B and P into an amorphous porous NiFe‐based electrocatalyst is reported, which possesses an amorphous porous metallic bulk structure and high corrosion resistance, and overcomes the issues associated with currently used catalyst nanomaterials. The PB codoping in the activated NiFePB (a‐NiFePB) delocalizes both Fe and Ni at Fermi energy level and enhances p–d hybridization as simulated by density functional theory calculations. The harmonized electronic structure and unique porous framework of the a‐NiFePB consequently improve the OER activity. The activated NiFePB thus exhibits an extraordinarily low overpotential of 197 mV for harvesting 10 mA cm⁻²OER current density and 233 mV for reaching 100 mA cm⁻²under chronopotentiometry condition, with the Tafel slope harmoniously conforming to 34 mV dec⁻¹. Impressive long‐term stability of this new catalyst is evidenced by only limited activity decay after 1400 h operation at 100 mA cm⁻². This work strategically directs a way for heading up a promising energy conversion alternative.