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  1. Using a three-wave longitudinal data set of Mexican-origin adolescents (N = 602, Mage = 12.92, SD = 0.91 at Wave 1), this study examines parallel pathways from early exposure to ethnic discrimination and drug-using peers, separately, to underage drinking status by late adolescence. Negative affect was expected to mediate the link from ethnic discrimination to underage drinking status (the stress-induced pathway), whereas social alcohol expectancy was expected to mediate the link from drug-using peers to underage drinking status (the socialization pathway). Our findings lend support to the stress-induced pathway while controlling for the socialization pathway. For the stress-induced pathway, wemore »found that early ethnic discrimination experiences were related to higher likelihood of having engaged in underage drinking by late adolescence through elevated negative affect sustained across adolescence. For the socialization pathway, we found no association between affiliation with drug-using peers in early adolescence and underage drinking status, either directly or indirectly. Present findings highlight the unique role of early ethnic discrimination experiences in underage drinking among Mexican-origin adolescents, over and above the effect of drug-using peers. Alcohol use interventions targeting ethnic minority adolescents should account for adolescents' ethnic discrimination experiences by helping adolescents develop adaptive coping strategies to handle negative affect induced by discrimination (e.g., reappraisal) rather than using alcohol to self-medicate.« less
  2. Many sequential decision making tasks can be viewed as combinatorial optimiza- tion problems over a large number of actions. When the cost of evaluating an ac- tion is high, even a greedy algorithm, which iteratively picks the best action given the history, is prohibitive to run. In this paper, we aim to learn a greedy heuris- tic for sequentially selecting actions as a surrogate for invoking the expensive oracle when evaluating an action. In particular, we focus on a class of combinato- rial problems that can be solved via submodular maximization (either directly on the objective function or via submodularmore »surrogates). We introduce a data-driven optimization framework based on the submodular-norm loss, a novel loss func- tion that encourages the resulting objective to exhibit diminishing returns. Our framework outputs a surrogate objective that is efficient to train, approximately submodular, and can be made permutation-invariant. The latter two properties al- low us to prove strong approximation guarantees for the learned greedy heuristic. Furthermore, our model is easily integrated with modern deep imitation learning pipelines for sequential prediction tasks. We demonstrate the performance of our algorithm on a variety of batched and sequential optimization tasks, including set cover, active learning, and data-driven protein engineering.« less
  3. We study the problem of learning sequential decision-making policies in settings with multiple state-action representations. Such settings naturally arise in many domains, such as planning (e.g., multiple integer programming formulations) and various combinatorial optimization problems (e.g., those with both integer programming and graph-based formulations). Inspired by the classical co-training framework for classification, we study the problem of co-training for policy learning. We present sufficient conditions under which learning from two views can improve upon learning from a single view alone. Motivated by these theoretical insights, we present a meta-algorithm for co-training for sequential decision making. Our framework is compatible withmore »both reinforcement learning and imitation learning. We validate the effectiveness of our approach across a wide range of tasks, including discrete/continuous control and combinatorial optimization.« less
  4. We advance a tripartite framework of language use to encompass language skills, the practice of language skills, and the subjective experiences associated with language use among Mexican-origin adolescents who function as language brokers by translating and interpreting for their English-limited parents. Using data collected over 2 waves from a sample of 604 adolescents (Wave 1: Mage = 12.41, SD = 0.97), this study identified 4 types of bilingual language broker profiles that capture the tripartite framework of language use: efficacious, moderate, ambivalent, and nonchalant. All 4 profiles emerged across waves and brokering recipients (i.e., mothers, fathers), except for Wave 1more »brokering for mother, in which case only 3 profiles (i.e., efficacious, moderate, and ambivalent) emerged. Three profiles emerged across time: stable efficacious, stable moderate, and other. The efficacious and stable efficacious profiles showed the most consistent relation to adolescents' academic competence. Improving bilingual language proficiency, together with fostering more frequently positive brokering experiences, may be an avenue to improving academic competence among Mexican-origin adolescents in the United States.« less
  5. Free, publicly-accessible full text available December 1, 2022