arXiv:2503.09008v3 Announce Type: replace
Abstract: Long-range dependencies are critical for effective graph representation learning, yet most existing datasets focus on small graphs tailored to inductive tasks, offering limited insight into long-rang…
arXiv:2603.28652v1 Announce Type: new
Abstract: Federated Learning (FL) is witnessing wider adoption due to its ability to benefit from large amounts of scattered data while preserving privacy. However, despite its advantages, federated learning suffe…
arXiv:2502.07297v3 Announce Type: replace
Abstract: High failure rates in cardiac drug development necessitate virtual clinical trials via electrocardiogram (ECG) generation to reduce risks and costs. However, existing ECG generation models struggle t…
arXiv:2603.28650v1 Announce Type: new
Abstract: Can a safety gate permit unbounded beneficial self-modification while maintaining bounded cumulative risk? We formalize this question through dual conditions — requiring sum delta_n < infinity (bounded …
arXiv:2501.10677v3 Announce Type: replace
Abstract: The advent of artificial intelligence has significantly enhanced credit scoring technologies. Despite the remarkable efficacy of advanced deep learning models, mainstream adoption continues to favor …
arXiv:2603.28611v1 Announce Type: new
Abstract: Fixed representational capacity is a fundamental constraint in continual learning: practitioners must guess an appropriate model width before training, without knowing how many distinct concepts the data…
arXiv:2501.00200v2 Announce Type: replace
Abstract: Recently, cutting-plane methods such as GCP-CROWN have been explored to enhance neural network verifiers and made significant advances. However, GCP-CROWN currently relies on generic cutting planes (…
arXiv:2603.28597v1 Announce Type: new
Abstract: The demand for Explainable AI (XAI) has triggered an explosion of methods, producing a landscape so fragmented that we now rely on surveys of surveys. Yet, fundamental challenges persist: conflicting met…
arXiv:2410.05493v2 Announce Type: replace
Abstract: We study transformers’ in-context learning of variable-length Markov chains (VOMCs), focusing on the finite-sample accuracy as the number of in-context examples increases. Compared to fixed-order Mar…
arXiv:2603.28595v1 Announce Type: new
Abstract: Although actor-critic methods have been successful in practice, their theoretical analyses have several limitations. Specifically, existing theoretical work either sidesteps the exploration problem by ma…