In the era of big data, many big organizations are integrating machine learning into their work pipelines to facilitate data analysis. However, the performance of their trained models is often restricted by limited and imbalanced data available to them. In this work, we develop an assisted learning framework for assisting organizations to improve their learning performance. The organizations have sufficient computation resources but are subject to stringent data-sharing and collaboration policies. Their limited imbalanced data often cause biased inference and sub-optimal decision-making. In assisted learning, an organizational learner purchases assistance service from an external service provider and aims to enhance its model performance within only a few assistance rounds. We develop effective stochastic training algorithms for both assisted deep learning and assisted reinforcement learning. Different from existing distributed algorithms that need to transmit gradients or models frequently, our framework allows the learner to only occasionally share information with the service provider, but still, obtain a model that achieves near-oracle performance as if all the data were centralized.

Whereas the recent emergence of large language models (LLMs) like ChatGPT has exhibited impressive general performance, it still has a large gap with fully-supervised models on specific tasks such as multi-span question answering. Previous researches found that in-context learning is an effective approach to exploiting LLM, by using a few task-related labeled data as demonstration examples to construct a few-shot prompt for answering new questions. A popular implementation is to concatenate a few questions and their correct answers through simple templates, informing LLM of the desired output. In this paper, we propose a novel way of employing labeled data such that it also informs LLM of some undesired output, by extending demonstration examples with feedback about answers predicted by an off-the-shelf model, e.g., correct, incorrect, or incomplete. Experiments on three multi-span question answering datasets as well as a keyphrase extraction dataset show that our new prompting strategy consistently improves LLM's in-context learning performance.

Rapid developments in data collecting devices and computation platforms produce an emerging number of learners and data modalities in many scientific domains. We consider the setting in which each learner holds a pair of parametric statistical model and a specific data source, with the goal of integrating information across a set of learners to enhance the prediction accuracy of a specific learner. One natural way to integrate information is to build a joint model across a set of learners that shares common parameters of interest. However, the parameter sharing patterns across a set of learners are not known a priori. Misspecifying the parameter sharing patterns and the parametric statistical model for each learner yields a biased estimator and degrades the prediction accuracy of the joint model. In this paper, we propose a novel framework for integrating information across a set of learners that is robust against model misspecification and misspecified parameter sharing patterns. The main crux is to sequentially incorporates additional learners that can enhance the prediction accuracy of an existing joint model based on a user-specified parameter sharing patterns across a set of learners, starting from a model with one learner. Theoretically, we show that the proposed method can data-adaptively select the correct parameter sharing patterns based on a user-specified parameter sharing patterns, and thus enhances the prediction accuracy of a learner. Extensive numerical studies are performed to evaluate the performance of the proposed method.