Transfer learning is a popular strategy to leverage external knowledge and improve statistical efficiency, particularly with a limited target sample. We propose a novel knowledge-guided Wasserstein Distributionally Robust Optimization (KG-WDRO) framework that adaptively incorporates multiple sources of external knowledge to overcome the conservativeness of vanilla WDRO, which often results in overly pessimistic shrinkage toward zero. Our method constructs smaller Wasserstein ambiguity sets by controlling the transportation along directions informed by the source knowledge. This strategy can alleviate perturbations on the predictive projection of the covariates and protect against information loss. Theoretically, we establish the equivalence between our WDRO formulation and the knowledge-guided shrinkage estimation based on collinear similarity, ensuring tractability and geometrizing the feasible set. This also reveals a novel and general interpretation for recent shrinkage-based transfer learning approaches from the perspective of distributional robustness. In addition, our framework can adjust for scaling differences in the regression models between the source and target and accommodates general types of regularization such as lasso and ridge. Extensive simulations demonstrate the superior performance and adaptivity of KG-WDRO in enhancing small-sample transfer learning.

We study continual event extraction, which aims to extract incessantly emerging event information while avoiding forgetting. We observe that the semantic confusion on event types stems from the annotations of the same text being updated over time. The imbalance between event types even aggravates this issue. This paper proposes a novel continual event extraction model with semantic confusion rectification. We mark pseudo labels for each sentence to alleviate semantic confusion. We transfer pivotal knowledge between current and previous models to enhance the understanding of event types. Moreover, we encourage the model to focus on the semantics of long-tailed event types by leveraging other associated types. Experimental results show that our model outperforms state-of-the-art baselines and is proficient in imbalanced datasets.

This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023. The challenge was centered around semantic segmentation in urban environments, with a particular focus on natural adversarial scenarios. The report presents the results of 19 submitted entries, with numerous techniques drawing inspiration from cutting-edge uncertainty quantification methodologies presented at prominent conferences in the fields of computer vision and machine learning and journals over the past few years. Within this document, the challenge is introduced, shedding light on its purpose and objectives, which primarily revolved around enhancing the robustness of semantic segmentation in urban scenes under varying natural adversarial conditions. The report then delves into the top-performing solutions. Moreover, the document aims to provide a comprehensive overview of the diverse solutions deployed by all participants. By doing so, it seeks to offer readers a deeper insight into the array of strategies that can be leveraged to effectively handle the inherent uncertainties associated with autonomous driving and semantic segmentation, especially within urban environments.

Therefore, the continual Heist and Paulheim, 2017; Zhang et al., 2018) few-shot RE paradigm (Qin and Joty, 2022) mainly assume a fixed pre-defined relation set and was proposed to simulate real human learning scenarios, train on a fixed dataset. However, they cannot work where new knowledge can be acquired from well with the new relations that continue emerging a small number of new samples. As illustrated in in some real-world scenarios of RE. Continual Figure 1, the continual few-shot RE paradigm expects RE (Wang et al., 2019; Han et al., 2020; Wu et al., the model to continuously learn new relations 2021) was proposed as a new paradigm to solve through abundant training data only for the first this situation, which applies the idea of continual task, but through sparse training data for all subsequent learning (Parisi et al., 2019) to the field of RE. tasks. Thus, the model needs to identify Compared with conventional RE, continual RE the growing relations well with few labeled data is more challenging. It requires the model to learn for them while retaining the knowledge on old relations emerging relations while maintaining a stable and without re-training from scratch. As relations accurate classification of old relations, i.e., the socalled grow, the confusion about relation representations catastrophic forgetting problem (Thrun and leads to catastrophic forgetting.