arXiv:2502.03393v5 Announce Type: replace Abstract: Accurate epidemic forecasting is crucial for outbreak preparedness, but existing data-driven models are often brittle. Typically trained on a single pathogen, they struggle with data scarcity during new outbreaks and fail under distribution shifts caused…
arXiv:2408.05854v5 Announce Type: replace-cross Abstract: Goodness-of-fit testing is often criticized for its lack of practical relevance: since “all models are wrong”, the null hypothesis that the data conform to our model is ultimately always rejected as the sample size grows.…
arXiv:2505.24161v2 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) offer low-latency and energy-efficient decision making on neuromorphic hardware, making them attractive for Reinforcement Learning (RL) in resource-constrained edge devices. However, most RL algorithms for continuous control are designed for Artificial…
arXiv:2510.19933v1 Announce Type: new Abstract: The Muon optimizer has rapidly emerged as a powerful, geometry-aware alternative to AdamW, demonstrating strong performance in large-scale training of neural networks. However, a critical theory-practice disconnect exists: Muon’s efficiency relies on fast, approximate orthogonalization,…
arXiv:2510.19934v1 Announce Type: new Abstract: Differentially private (DP) decentralized Federated Learning (FL) allows local users to collaborate without sharing their data with a central server. However, accurately quantifying the privacy budget of private FL algorithms is challenging due to the…
arXiv:2510.19896v1 Announce Type: new Abstract: In this paper, we propose an approach to support the diagnosis of urinary tract diseases, with a focus on bladder cancer, using SHAP (SHapley Additive exPlanations)-based feature selection to enhance the transparency and effectiveness of…
arXiv:2510.19917v1 Announce Type: new Abstract: ”Noisy” datasets (regimes with low signal to noise ratios, small sample sizes, faulty data collection, etc) remain a key research frontier for classification methods with both theoretical and practical implications. We introduce FINDER, a rigorous…
arXiv:2510.19893v1 Announce Type: new Abstract: Medical artificial intelligence systems have achieved remarkable diagnostic capabilities, yet they consistently exhibit performance disparities across demographic groups, causing real-world harm to underrepresented populations. While recent multimodal reasoning foundation models have advanced clinical diagnosis through…
arXiv:2510.19889v1 Announce Type: new Abstract: The traffic assignment problem is essential for traffic flow analysis, traditionally solved using mathematical programs under the Equilibrium principle. These methods become computationally prohibitive for large-scale networks due to non-linear growth in complexity with the…
arXiv:2510.19873v1 Announce Type: new Abstract: Despite significant evolution of CUDA programming and domain-specific libraries, effectively utilizing GPUs with massively parallel engines remains difficult. Large language models (LLMs) show strong potential in generating optimized CUDA code from sequential code. However, using…
