Search

Incorporating unanswerable questions into EHR QA systems is crucial for testing the trustworthiness of a system, as providing non-existent responses can mislead doctors in their diagnoses. The EHRSQL dataset stands out as a promising benchmark because it is the only dataset that incorporates unanswerable questions in the EHR QA system alongside practical questions. However, in this work, we identify a data bias in these unanswerable questions; they can often be discerned simply by filtering with specific N-gram patterns. Such biases jeopardize the authenticity and reliability of QA system evaluations. To tackle this problem, we propose a simple debiasing method of adjusting the split between the validation and test sets to neutralize the undue influence of N-gram filtering. By experimenting on the MIMIC-III dataset, we demonstrate both the existing data bias in EHRSQL and the effectiveness of our data split strategy in mitigating this bias., Comment: DPFM Workshop, ICLR 2024

Although federated learning has made awe-inspiring advances, most studies have assumed that the client's data are fully labeled. However, in a real-world scenario, every client may have a significant amount of unlabeled instances. Among the various approaches to utilizing unlabeled data, a federated active learning framework has emerged as a promising solution. In the decentralized setting, there are two types of available query selector models, namely 'global' and 'local-only' models, but little literature discusses their performance dominance and its causes. In this work, we first demonstrate that the superiority of two selector models depends on the global and local inter-class diversity. Furthermore, we observe that the global and local-only models are the keys to resolving the imbalance of each side. Based on our findings, we propose LoGo, a FAL sampling strategy robust to varying local heterogeneity levels and global imbalance ratio, that integrates both models by two steps of active selection scheme. LoGo consistently outperforms six active learning strategies in the total number of 38 experimental settings., Comment: CVPR 2023

Federated Learning (FL) aggregates locally trained models from individual clients to construct a global model. While FL enables learning a model with data privacy, it often suffers from significant performance degradation when clients have heterogeneous data distributions. This data heterogeneity causes the model to forget the global knowledge acquired from previously sampled clients after being trained on local datasets. Although the introduction of proximal objectives in local updates helps to preserve global knowledge, it can also hinder local learning by interfering with local objectives. To address this problem, we propose a novel method, Federated Stabilized Orthogonal Learning (FedSOL), which adopts an orthogonal learning strategy to balance the two conflicting objectives. FedSOL is designed to identify gradients of local objectives that are inherently orthogonal to directions affecting the proximal objective. Specifically, FedSOL targets parameter regions where learning on the local objective is minimally influenced by proximal weight perturbations. Our experiments demonstrate that FedSOL consistently achieves state-of-the-art performance across various scenarios., Comment: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (CVPR 2024)

Cell segmentation is a fundamental task for computational biology analysis. Identifying the cell instances is often the first step in various downstream biomedical studies. However, many cell segmentation algorithms, including the recently emerging deep learning-based methods, still show limited generality under the multi-modality environment. Weakly Supervised Cell Segmentation in Multi-modality High-Resolution Microscopy Images was hosted at NeurIPS 2022 to tackle this problem. We propose MEDIAR, a holistic pipeline for cell instance segmentation under multi-modality in this challenge. MEDIAR harmonizes data-centric and model-centric approaches as the learning and inference strategies, achieving a 0.9067 F1-score at the validation phase while satisfying the time budget. To facilitate subsequent research, we provide the source code and trained model as open-source: https://github.com/Lee-Gihun/MEDIAR, Comment: NeurIPS 2022 Cell Segmentation Challenge

Robustness is becoming another important challenge of federated learning in that the data collection process in each client is naturally accompanied by noisy labels. However, it is far more complex and challenging owing to varying levels of data heterogeneity and noise over clients, which exacerbates the client-to-client performance discrepancy. In this work, we propose a robust federated learning method called FedRN, which exploits k-reliable neighbors with high data expertise or similarity. Our method helps mitigate the gap between low- and high-performance clients by training only with a selected set of clean examples, identified by their ensembled mixture models. We demonstrate the superiority of FedRN via extensive evaluations on three real-world or synthetic benchmark datasets. Compared with existing robust training methods, the results show that FedRN significantly improves the test accuracy in the presence of noisy labels., Comment: Accepted to CIKM 2022

Federated learning has evolved to improve a single global model under data heterogeneity (as a curse) or to develop multiple personalized models using data heterogeneity (as a blessing). However, little research has considered both directions simultaneously. In this paper, we first investigate the relationship between them by analyzing Federated Averaging at the client level and determine that a better federated global model performance does not constantly improve personalization. To elucidate the cause of this personalization performance degradation problem, we decompose the entire network into the body (extractor), which is related to universality, and the head (classifier), which is related to personalization. We then point out that this problem stems from training the head. Based on this observation, we propose a novel federated learning algorithm, coined FedBABU, which only updates the body of the model during federated training (i.e., the head is randomly initialized and never updated), and the head is fine-tuned for personalization during the evaluation process. Extensive experiments show consistent performance improvements and an efficient personalization of FedBABU. The code is available at https://github.com/jhoon-oh/FedBABU., Comment: Published at ICLR 2022

The growth in international research on public service motivation (PSM) raises a number of important questions about the degree to which the theory and research developed in one country can contribute to our understanding of PSM in other counties. To help address this issue, this study revisits the conceptual and operational definitions of PSM to address weaknesses previously noted in the literature. Although some important steps have been taken to both improve and internationalize the PSM scale, this work has been done incrementally. In contrast, this study takes a more systematic and comprehensive approach by combining the efforts of international PSM scholars to develop and then test a revised measurement instrument for PSM in 12 countries. Although the resulting four dimensional 16-item measure of PSM reported here provides a better theoretical and empirical foundation for the measurement of PSM, our results suggest that the exact meaning and scaling of PSM dimensions are likely to differ across cultures and languages. These results raise serious concerns regarding the ability to develop a single universal scale of PSM, or making direct comparisons of PSM across countries

The growth in international research on public service motivation (PSM) raises a number of important questions about the degree to which the theory and research developed in one country can contribute to our understanding of PSM in other counties. To help address this issue, this study revisits the conceptual and operational definitions of PSM to address weaknesses previously noted in the literature. Although some important steps have been taken to both improve and internationalize the PSM scale, this work has been done incrementally. In contrast, this study takes a more systematic and comprehensive approach by combining the efforts of international PSM scholars to develop and then test a revised measurement instrument for PSM in 12 countries. Although the resulting four dimensional 16-item measure of PSM reported here provides a better theoretical and empirical foundation for the measurement of PSM, our results suggest that the exact meaning and scaling of PSM dimensions are likely to differ across cultures and languages. These results raise serious concerns regarding the ability to develop a single universal scale of PSM, or making direct comparisons of PSM across countries. © 2012 The Author.

The growth in international research on public service motivation (PSM) raises a number of important questions about the degree to which the theory and research developed in one country can contribute to our understanding of PSM in other counties. To help address this issue, this study revisits the conceptual and operational definitions of PSM to address weaknesses previously noted in the literature. Although some important steps have been taken to both improve and internationalize the PSM scale, this work has been done incrementally. In contrast, this study takes a more systematic and comprehensive approach by combining the efforts of international PSM scholars to develop and then test a revised measurement instrument for PSM in 12 countries. Although the resulting four dimensional 16-item measure of PSM reported here provides a better theoretical and empirical foundation for the measurement of PSM, our results suggest that the exact meaning and scaling of PSM dimensions are likely to differ across cultures and languages. These results raise serious concerns regarding the ability to develop a single universal scale of PSM, or making direct comparisons of PSM across countries. © 2012 The Author.

The growth in international research on public service motivation (PSM) raises a number of important questions about the degree to which the theory and research developed in one country can contribute to our understanding of PSM in other counties. To help address this issue, this study revisits the conceptual and operational definitions of PSM to address weaknesses previously noted in the literature. Although some important steps have been taken to both improve and internationalize the PSM scale, this work has been done incrementally. In contrast, this study takes a more systematic and comprehensive approach by combining the efforts of international PSM scholars to develop and then test a revised measurement instrument for PSM in 12 countries. Although the resulting four dimensional 16-item measure of PSM reported here provides a better theoretical and empirical foundation for the measurement of PSM, our results suggest that the exact meaning and scaling of PSM dimensions are likely to differ across cultures and languages. These results raise serious concerns regarding the ability to develop a single universal scale of PSM, or making direct comparisons of PSM across countries. © 2012 The Author.

The growth in international research on public service motivation (PSM) raises a number of important questions about the degree to which the theory and research developed in one country can contribute to our understanding of PSM in other counties. To help address this issue, this study revisits the conceptual and operational definitions of PSM to address weaknesses previously noted in the literature. Although some important steps have been taken to both improve and internationalize the PSM scale, this work has been done incrementally. In contrast, this study takes a more systematic and comprehensive approach by combining the efforts of international PSM scholars to develop and then test a revised measurement instrument for PSM in 12 countries. Although the resulting four dimensional 16-item measure of PSM reported here provides a better theoretical and empirical foundation for the measurement of PSM, our results suggest that the exact meaning and scaling of PSM dimensions are likely to differ across cultures and languages. These results raise serious concerns regarding the ability to develop a single universal scale of PSM, or making direct comparisons of PSM across countries