Learning with noisy labels (LNL) is essential for training deep neural networks with imperfect data. Meta-learning approaches have achieved success by using a clean unbiased labeled set to train a robust model. However, this approach heavily depends on the availability of a clean labeled meta-dataset, which is difficult to obtain in practice. In this work, we thus tackle the challenge of meta-learning for noisy label scenarios without relying on a clean labeled dataset. Our approach leverages the data itself while bypassing the need for labels. Building on the insight that clean samples effectively preserve the consistency of related data structures across the last hidden and the final layer, whereas noisy samples disrupt this consistency, we design the Cross-layer Information Divergence-based Meta Update Strategy (CLID-MU). CLID-MU leverages the alignment of data structures across these diverse feature spaces to evaluate model performance and use this alignment to guide training. Experiments on benchmark datasets with varying amounts of labels under both synthetic and real-world noise demonstrate that CLID-MU outperforms state-of-the-art methods. The code is released at https://github.com/ruofanhu/CLID-MU.

Foodborne illnesses pose a threat to public health, leading to morbidity, mortality, and economic burden annually. Social media, while providing a rich timely source for training AI models for surveillance, requires effective tools for annotation. While Large Language Models (LLMs) have shown promise for generating simple labels, here hierarchical labels composed of entity types like food type and symptom (at individual word level) and the foodborne illness event (at complete post level) are required. For this, we introduce ICL2FID, the first LLM-based hierarchical labeling framework designed to annotate social media posts for foodborne illness detection at two levels using only a few demonstration examples. To utilize the interconnection between post and word levels, ICL2FID instructs the LLM to leverage information from one level when predicting the other level. To combat model hallucination and cyclic dependencies, a verification step improves evidence propagation between interconnected word and post-level labeling tasks. Strategies for custom selection of demonstration examples are designed reducing biases and increasing representation. We compare ICL2FID against traditional supervised learning and other LLM methods, demonstrating that it not only achieves superior accuracy but does so at a fraction of the cost and time. These findings highlight ICL2FID’s potential as a viable alternative for hierarchical label generation in scenarios with limited resources and huge data sets. Code is available at https://github.com/zdy93/ICL2FID.

Deep neural networks (DNNs) have advanced many machine learning tasks, but their performance is often harmed by noisy labels in real-world data. Addressing this, we introduce CoLafier, a novel approach that uses Local Intrinsic Dimensionality (LID) for learning with noisy labels. CoLafier consists of two subnets: LID-dis and LID-gen. LID-dis is a specialized classifier. Trained with our uniquely crafted scheme, LID-dis consumes both a sample's features and its label to predict the label - which allows it to produce an enhanced internal representation. We observe that LID scores computed from this representation that effectively distinguish between correct and incorrect labels across various noise scenarios. In contrast to LID-dis, LID-gen, functioning as a regular classifier, operates solely on the sample's features. During training, CoLafier utilizes two augmented views per instance to feed both subnets. CoLafier considers the LID scores from the two views as produced by LID-dis to assign weights in an adapted loss function for both subnets. Concurrently, LID-gen, serving as classifier, suggests pseudo-labels. LID-dis then processes these pseudo-labels along with two views to derive LID scores. Finally, these LID scores along with the differences in predictions from the two subnets guide the label update decisions. This dual-view and dual-subnet approach enhances the overall reliability of the framework. Upon completion of the training, we deploy the LID-gen subnet of CoLafier as the final classification model. CoLafier demonstrates improved prediction accuracy, surpassing existing methods, particularly under severe label noise. For more details, see the code at https://github.com/zdy93/CoLafier.

Foodborne illnesses significantly impact public health. Deep learning surveillance applications using social media data aim to detect early warning signals. However, labeling foodborne illness-related tweets for model training requires extensive human resources, making it challenging to collect a sufficient number of high-quality labels for tweets within a limited budget. The severe class imbalance resulting from the scarcity of foodborne illness-related tweets among the vast volume of social media further exacerbates the problem. Classifiers trained on a classimbalanced dataset are biased towards the majority class, making accurate detection difficult. To overcome these challenges, we propose EGAL, a deep learning framework for foodborne illness detection that uses small expert-labeled tweets augmented by crowdsourced-labeled and massive unlabeled data. Specifically, by leveraging tweets labeled by experts as a reward set, EGAL learns to assign a weight of zero to incorrectly labeled tweets to mitigate their negative influence. Other tweets receive proportionate weights to counter-balance the unbalanced class distribution. Extensive experiments on real-world TWEET-FID data show that EGAL outperforms strong baseline models across different settings, including varying expert-labeled set sizes and class imbalance ratios. A case study on a multistate outbreak of Salmonella Typhimurium infection linked to packaged salad greens demonstrates how the trained model captures relevant tweets offering valuable outbreak insights. EGAL, funded by the U.S. Department of Agriculture (USDA), has the potential to be deployed for real-time analysis of tweet streaming, contributing to foodborne illness outbreak surveillance efforts.

Sequential tabular data is one of the most commonly used data types in real-world applications. Different from conventional tabular data, where rows in a table are independent, sequential tabular data contains rich contextual and sequential information, where some fields aredynamically changing over time and others arestatic. Existing transformer-based approaches analyzing sequential tabular data overlook the differences between dynamic and static fields by replicating and filling static fields into each record, and ignore temporal information between rows, which leads to three major disadvantages: (1) computational overhead, (2) artificially simplified data for masked language modeling pre-training task that may yield less meaningful representations, and (3) disregarding the temporal behavioral patterns implied by time intervals. In this work, we propose FATA-Trans, a model with two field transformers for modeling sequential tabular data, where each processes static and dynamic field information separately. FATA-Trans isfield - andtime -aware for sequential tabular data. Thefield -type embedding in the method enables FATA-Trans to capture differences between static and dynamic fields. Thetime -aware position embedding exploits both order and time interval information between rows, which helps the model detect underlying temporal behavior in a sequence. Our experiments on three benchmark datasets demonstrate that the learned representations from FATA-Trans consistently outperform state-of-the-art solutions in the downstream tasks. We also present visualization studies to highlight the insights captured by the learned representations, enhancing our understanding of the underlying data. Our codes are available at https://github.com/zdy93/FATA-Trans.

Human-like explanation for text classification is essential for high-impact settings such as healthcare where human rationales are required to support specialists’ decisions. Conventional approaches learn explanations using attention mechanisms to assign heavy weights to words that have a high impact on a model’s prediction. However, such heavily-weighted words often do not reflect human intuition. To advance human rationale, recent studies propose to supervise attention mechanisms assuming access to a huge set of attention labels collected from humans, called human attention maps (HAMs). Unfortunately, acquiring such HAMs for a huge dataset is very tedious, error-prone, and expensive in practice. Thus, we propose the novel problem of text classification with limited human attention supervision. Specifically, we study the learning of human-like attention weights from a dataset in which all documents contain classification labels but only a few documents provide HAMs. To this end, we design a deep learning architecture, HELAS: Human-like Explanation with Limited Attention Supervision to adaptively learn attention weights that focus on words analogous to a human with very limited attention supervision. HELAS effectively unifies joint learning improving both tasks of text classification and humanlike explanation even with only insufficient supervision labels for the latter task. Our experiments show that HELAS generates attention maps similar to real human annotations raising similarity scores up to 22% over state-of-the-art alternatives, even with as little as 2% of the documents having HAMs. It concurrently improves text classification by driving accuracy up to 19% over four state-of-the-art methods.