Multiview datasets are common in scientific and engineering applications, yet existing fusion methods offer limited theoretical guarantees, particularly in the presence of heterogeneous and high-dimensional noise. We propose Generalized Robust Adaptive-Bandwidth Multiview Diffusion Maps (GRAB-MDM), a new kernel-based diffusion geometry framework for integrating multiple noisy data sources. The key innovation of GRAB-MDM is a {view}-dependent bandwidth selection strategy that adapts to the geometry and noise level of each view, enabling a stable and principled construction of multiview diffusion […]

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Health checks are a set of checks (duh) that you perform in order to tell whether an application/service is up, running & healthy or not. It’s usually one or more endpoints that reports the status, the response differs from language/framework to an other. Health checks are very useful especially when your application depends on other things like a database or even other services.

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Recently, there have been numerous attempts to enhance the sample efficiency of off-policy reinforcement learning (RL) agents when interacting with the environment, including architecture improvements and new algorithms. Despite these advances, they overlook the potential of directly constraining the initial representations of the input data, which can intuitively alleviate the distribution shift issue and stabilize training. In this paper, we introduce the Tanh function into the initial layer to fulfill such a constraint. We theoretically unpack the convergence […]

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arXiv:2309.13183v3 Announce Type: replace-cross Abstract: Information Value (IV) is a widely used technique for feature selection prior to the modeling phase, particularly in credit scoring and related domains. However, conventional IV-based practices rely on fixed empirical thresholds, which lack statistical justification and may be sensitive to characteristics such as class imbalance. In this work, we develop a formal statistical framework for IV by establishing its connection with Jeffreys divergence and propose a novel nonparametric hypothesis test, referred to […]

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Clark Packard Last week, Disney+ released a restored version of The Beatles Anthology, the landmark 1995 ABC eight-part documentary series that remains the closest thing to a definitive account of the band’s extraordinary journey. I binge-watched the updated Anthology over the Thanksgiving holiday weekend, which brought back memories of watching the original with my parents 30 years ago. Now I’m the parent, though my wife and four-year-old son were less enthusiastic about the marathon viewing sessions. Last year, I […]

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:::info Astounding Stories of Super-Science October, 1994, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. The Picture of Dorian Gray – Chapter XI Astounding Stories of Super-Science October 1994: The Picture of Dorian Gray – Chapter XI By Oscar Wilde ::: For years, Dorian Gray could not free himself from the influence of this book. Or perhaps it would be more accurate to say that he […]

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arXiv:2603.20230v1 Announce Type: new Abstract: Autonomous driving involves multiple, often conflicting objectives such as safety, efficiency, and comfort. In reinforcement learning (RL), these objectives are typically combined through weighted summation, which collapses their relative priorities and often yields policies that violate safety-critical constraints. To overcome this limitation, we introduce the Preordered Multi-Objective MDP (Pr-MOMDP), which augments standard MOMDPs with a preorder over reward components. This structure enables reasoning about actions with respect to a hierarchy of objectives rather […]

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Machine learning has become a powerful tool for discovering governing laws of dynamical systems from data. However, most existing approaches degrade severely when observations are sparse, noisy, or irregularly sampled. In this work, we address the problem of learning symbolic representations of nonlinear Hamiltonian dynamical systems under extreme data scarcity by explicitly incorporating physical structure into the learning architecture. We introduce Adaptable Symplectic Recurrent Neural Networks (ASRNNs), a parameter-cognizant, structure-preserving model that combines Hamiltonian learning with symplectic recurrent […]

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