10 accepted papers
Group fairness can ensure equitable performance across different demographic subgroups for medical image analysis. However, the current fine-tuned foundation models (FMs) exhibit significant subgroup disparity. One-shot federated learning (OFL) can potentially mitigate this by leveraging cross-insti…
Electrocardiography (ECG) is a cornerstone of cardiac assessment, making the learning of informative ECG representations fundamental to tasks ranging from disease diagnosis to clinical report generation. However, existing methods operate almost exclusively in the observable ECG signal space. In prac…
Vision-Language Models (VLMs) frequently suffer from visual perception errors and hallucinations that compromise answer accuracy in complex reasoning tasks. Reinforcement Learning with Verifiable Rewards (RLVR) offers a promising solution by optimizing policies using answer correctness signals. Desp…
Accurate forecasting of resource consumption, such as gas, is essential for efficient energy management, cost reduction, and sustainability. Time series forecasting (TSF) techniques like recurrent neural networks (RNNs), convolutional networks (TCNs), and Transformers have been employed to model com…
Auction-based Federated Learning (AFL) has emerged as an important research field in recent years. The prevailing strategies for FL data consumers (DCs) assume that the entire team of the required data owners (DOs) for an FL task must be assembled before training can commence. In practice, a DC can…
In multivariate time series forecasting, transformer-based methods have gained attention for their ability to capture complex dependencies and are often integrated with graph neural networks to improve forecasting performance. However, these approaches are computationally intensive. Mamba, a more co…
The zero-shot retrieval task aims to retrieve the most relevant documents to a user’s query without relevance labels. Current approaches expand input queries by generating pseudo-documents with large language models (LLMs) and perform document retrieval based on the expanded queries. However, their…
Auction-based Federated Learning (AFL) is a burgeoning research area. However, existing bidding strategies for AFL data consumers (DCs) primarily focus on maximizing expected accumulated utility, disregarding the more complex goal of revenue maximization. They also only consider winning bids, leadin…
Gas usage estimation plays a critical role in various aspects of the power generation and delivery business, including budgeting, resource planning, and environmental preservation. Federated Learning (FL) has demonstrated its potential in enhancing the accuracy and reliability of gas usage estimatio…
Artificial intelligence (AI)-empowered industrial fault diagnostics is important in ensuring the safe operation of industrial applications. Since complex industrial systems often involve multiple industrial plants (possibly belonging to different companies or subsidiaries) with sensitive data collec…