arXiv:2504.04528v3 Announce Type: replace-cross Abstract: Machine learning-supported decisions, such as ordering diagnostic tests or determining preventive custody, often require converting probabilistic forecasts into binary classifications. We adopt a consequentialist perspective from decision theory to argue that evaluation methods should prioritize forecast quality across thresholds and base rates. This motivates the use of proper scoring rules such as the Brier score and log loss. However, our empirical review of practices at major ML venues (ICML, FAccT, CHIL) reveals a […]

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arXiv:2603.23637v1 Announce Type: new Abstract: Ray-tracing-based 3D Gaussian splatting (3DGS) methods overcome the limitations of rasterization — rigid pinhole camera assumptions, inaccurate shadows, and lack of native reflection or refraction — but remain slower due to the cost of sorting all intersecting Gaussians along every ray. Moreover, existing ray-tracing methods still rely on rasterization-style approximations such as shadow mapping for relightable scenes, undermining the generality that ray tracing promises. We present a differentiable, sorting-free stochastic formulation for ray-traced […]

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The Gielis superformula is a powerful parametric tool that generates an infinite variety of natural and organic curves and surfaces through a compact set of parameters. However, classical differential geometry has lacked a unified framework for analyzing their curvature, torsion, and intrinsic geometric properties. This study addresses this gap by developing a novel superelliptic geometric framework that integrates the superformula 6with the differential geometry of curves and surfaces. We define the superelliptic inner and cross products, the star […]

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arXiv:2602.13213v1 Announce Type: new Abstract: Commercial insurance underwriting is a labor-intensive process that requires manual review of extensive documentation to assess risk and determine policy pricing. While AI offers substantial efficiency improvements, existing solutions lack comprehensive reasoning capabilities and internal mechanisms to ensure reliability within regulated, high-stakes environments. Full automation remains impractical and inadvisable in scenarios where human judgment and accountability are critical. This study presents a decision-negative, human-in-the-loop agentic system that incorporates an adversarial self-critique mechanism as […]

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arXiv:2305.02657v5 Announce Type: replace Abstract: In this paper, we provide a strategy to determine the eigenvalue decay rate (EDR) of a large class of kernel functions defined on a general domain rather than $mathbb S^{d}$. This class of kernel functions include but are not limited to the neural tangent kernel associated with neural networks with different depths and various activation functions. After proving that the dynamics of training the wide neural networks uniformly approximated that of the neural […]

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arXiv:2602.04972v1 Announce Type: new Abstract: The adoption of generative AI in education has accelerated dramatically in recent years, with Large Language Models (LLMs) increasingly integrated into learning environments in the hope of providing personalized support that enhances learner engagement and knowledge retention. However, truly personalized support requires access to meaningful Learning Context (LC) regarding who the learner is, what they are trying to understand, and how they are engaging with the material. In this paper, we present a […]

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arXiv:2601.13519v3 Announce Type: replace Abstract: This paper introduces a new problem-dependent regret measure for online convex optimization with smooth losses. The notion, which we call the $G^star$ regret, depends on the cumulative squared gradient norm evaluated at the decision in hindsight. We show that the $G^star$ regret strictly refines the existing $L^star$ (small loss) regret, and that it can be arbitrarily sharper when the losses have vanishing curvature around the hindsight decision. We establish upper and lower bounds […]

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Facial expression recognition (FER) is a well-established task in computer vision, yet its application to non-photorealistic domains, such as anime and manga, remains largely underexplored. The stylized, exaggerated, and often non-proportional facial features of illustrated characters present unique challenges for deep learning models trained predominantly on realistic imagery. In this work, we construct a balanced dataset of 3,000 manga and anime face images spanning six emotion categories (Angry, Embarrassed, Happy, Psycho-Crazy, Sad, Scared) and conduct a systematic comparison […]

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