arXiv:2509.26605v1 Announce Type: cross Abstract: Deploying reinforcement learning (RL) in robotics, industry, and health care is blocked by two obstacles: the difficulty of specifying accurate rewards and the risk of unsafe, data-hungry exploration. We address this by proposing a two-stage…
arXiv:2509.25230v1 Announce Type: new Abstract: A useful inductive bias for temporal data is that trajectories should stay close to the data manifold. Traditional flow matching relies on straight conditional paths, and flow matching methods which learn geodesics rely on RBF…
arXiv:2404.06167v2 Announce Type: replace Abstract: Single-cell RNA sequencing (scRNA-seq) is essential for unraveling cellular heterogeneity and diversity, offering invaluable insights for bioinformatics advancements. Despite its potential, traditional clustering methods in scRNA-seq data analysis often neglect the structural information embedded in…
arXiv:2509.25231v1 Announce Type: new Abstract: Time series forecasting has various applications, such as meteorological rainfall prediction, traffic flow analysis, financial forecasting, and operational load monitoring for various systems. Due to the sparsity of time series data, relying solely on time-domain…
arXiv:2502.08457v2 Announce Type: replace Abstract: Bilevel optimization has emerged as a technique for addressing a wide range of machine learning problems that involve an outer objective implicitly determined by the minimizer of an inner problem. While prior works have primarily…
arXiv:2509.25232v1 Announce Type: new Abstract: We present a family of textit{Gaussian Mixture Approximation} (GMA) samplers for sampling unnormalised target densities, encompassing textit{weights-only GMA} (W-GMA), textit{Laplace Mixture Approximation} (LMA), textit{expectation-maximization GMA} (EM-GMA), and further variants. GMA adopts a simple two-stage paradigm:…
arXiv:2505.09308v2 Announce Type: replace Abstract: The Unconstrained Feature Model (UFM) is a mathematical framework that enables closed-form approximations for minimal training loss and related performance measures in deep neural networks (DNNs). This paper leverages the UFM to provide qualitative insights…
arXiv:2509.25233v1 Announce Type: new Abstract: Federated Learning (FL) is a distributed machine learning technique that preserves data privacy by sharing only the trained parameters instead of the client data. This makes FL ideal for highly dynamic, heterogeneous, and time-critical applications,…
arXiv:2507.07237v2 Announce Type: replace Abstract: Data driven approaches have the potential to make modeling complex, nonlinear physical phenomena significantly more computationally tractable. For example, computational modeling of fracture is a core challenge where machine learning techniques have the potential to…
arXiv:2509.25235v1 Announce Type: new Abstract: Correct identification of failure mechanisms is essential for manufacturers to ensure the quality of their products. Certain failures of printheads developed by Canon Production Printing can be identified from the behavior of individual nozzles, the…
