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…
arXiv:2512.21521v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative training on decentralized data. Differential privacy (DP) is crucial for FL, but current private methods often rely on unrealistic assumptions (e.g., bounded gradients or heterogeneity), hindering practical application. Existing works…
arXiv:2507.02227v2 Announce Type: replace Abstract: Neural networks have emerged as powerful surrogates for solving partial differential equations (PDEs), offering significant computational speedups over traditional methods. However, these models suffer from a critical limitation: error accumulation during long-term rollouts, where small…
arXiv:2512.21527v1 Announce Type: new Abstract: Conventional Reinforcement Learning (RL) algorithms, typically focused on estimating or maximizing expected returns, face challenges when refining offline pretrained models with online experiences. This paper introduces Generative Actor Critic (GAC), a novel framework that decouples…
arXiv:2509.20896v2 Announce Type: replace Abstract: We propose a deterministic denoising algorithm for discrete-state diffusion models based on Markov chains. The generative reverse process is derandomized by introducing a variant of the herding algorithm with weakly chaotic dynamics, which induces deterministic…
