arXiv:2603.22320v1 Announce Type: new Abstract: While climate models provide insights for climate decision-making, their use is constrained by significant computational and technical demands. Although machine learning (ML) emulators offer a way to bypass the high computational costs, their effective use…
arXiv:2603.22987v1 Announce Type: cross Abstract: Membership inference attacks (MIAs) aim to determine whether a data sample was included in a machine learning (ML) model’s training set and have become the de facto standard for measuring privacy leakages in ML. We…
arXiv:2603.22322v1 Announce Type: new Abstract: Machine learning systems deployed in medical devices require governance frameworks that ensure safety while enabling continuous improvement. Regulatory bodies including the FDA and European Union have introduced mechanisms such as the Predetermined Change Control Plan…
arXiv:2603.23136v1 Announce Type: cross Abstract: Automated knowledge graph (KG) construction is essential for navigating the rapidly expanding body of scientific literature. However, existing approaches struggle to recognize long multi-word entities, often fail to generalize across domains, and typically overlook the…
arXiv:2603.22323v1 Announce Type: new Abstract: Accurately predicting the state-of-health (SOH) and remaining useful life (RUL) of lithium-ion batteries is crucial for ensuring the safe and efficient operation of electric vehicles while minimizing associated risks. However, current deep learning methods are…
arXiv:2603.11858v2 Announce Type: replace Abstract: We propose a WiFi Channel State Information (CSI) sensing framework for multi-station deployments that addresses two fundamental challenges in practical CSI sensing: station-wise feature missingness and limited labeled data. Feature missingness is commonly handled by…
arXiv:2303.15604v2 Announce Type: replace-cross Abstract: Publicly available collections of drug-like molecules have grown to comprise 10s of billions of possibilities in recent history due to advances in chemical synthesis. Traditional methods for identifying “hit” molecules from a large collection of…
arXiv:2506.08916v2 Announce Type: replace Abstract: Agent-based modeling (ABM) is a powerful tool for understanding self-organizing biological systems, but it is computationally intensive and often not analytically tractable. Equation learning (EQL) methods can derive continuum models from ABM data, but they…
arXiv:2511.09211v3 Announce Type: replace Abstract: Clustering is a fundamental task in unsupervised learning, but most existing methods heavily rely on hyperparameters such as the number of clusters or other sensitive settings, limiting their applicability in real-world scenarios. To address this…
arXiv:2603.23205v1 Announce Type: cross Abstract: Standard conformal anomaly detection provides marginal finite-sample guarantees under the assumption of exchangeability . However, real-world data often exhibit distribution shifts, necessitating a weighted conformal approach to adapt to local non-stationarity. We show that this…
