arXiv:2512.21486v1 Announce Type: new Abstract: Functional tensor decomposition can analyze multi-dimensional data with real-valued indices, paving the path for applications in machine learning and signal processing. A limitation of existing approaches is the assumption that the tensor rank-a critical parameter…
arXiv:2512.19491v2 Announce Type: replace Abstract: Detecting fraud and corruption in public procurement remains a major challenge for governments worldwide. Most research to-date builds on domain-knowledge-based corruption risk indicators of individual contract-level features and some also analyzes contracting network patterns. A…
arXiv:2512.21506v1 Announce Type: new Abstract: As wearable sensing becomes increasingly pervasive, a key challenge remains: how can we generate natural language summaries from raw physiological signals such as actigraphy – minute-level movement data collected via accelerometers? In this work, we…
arXiv:2503.09626v2 Announce Type: replace-cross Abstract: Social bot detection is crucial for mitigating misinformation, online manipulation, and coordinated inauthentic behavior. While existing neural network-based detectors perform well on benchmarks, they struggle with generalization due to distribution shifts across datasets and frequently…
arXiv:2512.21510v1 Announce Type: new Abstract: Real-world multi-view data usually exhibits highly inconsistent missing patterns which challenges the effectiveness of incomplete multi-view clustering (IMVC). Although existing IMVC methods have made progress from both imputation-based and imputation-free routes, they have overlooked the…
arXiv:2512.06270v2 Announce Type: replace-cross Abstract: In this work, we study contextual strongly convex simulation optimization and adopt an “optimize then predict” (OTP) approach for real-time decision making. In the offline stage, simulation optimization is conducted across a set of covariates…
arXiv:2512.21515v1 Announce Type: new Abstract: Continual Pre-training (CPT) serves as a fundamental approach for adapting foundation models to domain-specific applications. Scaling laws for pre-training define a power-law relationship between dataset size and the test loss of an LLM. However, the…
arXiv:2405.04875v2 Announce Type: replace Abstract: Split Federated Learning (SFL) is a distributed machine learning framework which strategically divides the learning process between a server and clients and collaboratively trains a shared model by aggregating local models updated based on data…
arXiv:2512.21516v1 Announce Type: new Abstract: Recently, contrastive learning (CL) plays an important role in exploring complementary information for multi-view clustering (MVC) and has attracted increasing attention. Nevertheless, real-world multi-view data suffer from data incompleteness or noise, resulting in rare-paired samples…
arXiv:2502.17123v3 Announce Type: replace Abstract: The selection of penalty hyperparameters is a critical aspect in Nonnegative Matrix Factorization (NMF), since these values control the trade-off between reconstruction accuracy and adherence to desired constraints. In this work, we focus on an…
