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Tag: probabilistic

  • Probabilistic Multi-Variant Reasoning: Turning Fluent LLM Answers Into Weighted Options

    Probabilistic Multi-Variant Reasoning: Turning Fluent LLM Answers Into Weighted Options Human-guided AI collaboration The post Probabilistic Multi-Variant Reasoning: Turning Fluent LLM Answers Into Weighted Options appeared first on Towards Data Science. alan nekhom Go to original source

  • Generalized Inequality-based Approach for Probabilistic WCET Estimation

    Generalized Inequality-based Approach for Probabilistic WCET Estimation arXiv:2511.11682v1 Announce Type: new Abstract: Estimating the probabilistic Worst-Case Execution Time (pWCET) is essential for ensuring the timing correctness of real-time applications, such as in robot IoT systems and autonomous driving systems. While methods based on Extreme Value Theory (EVT) can provide tight bounds, they suffer from model…

  • Scalable h-adaptive probabilistic solver for time-independent and time-dependent systems

    Scalable h-adaptive probabilistic solver for time-independent and time-dependent systems arXiv:2508.09623v1 Announce Type: new Abstract: Solving partial differential equations (PDEs) within the framework of probabilistic numerics offers a principled approach to quantifying epistemic uncertainty arising from discretization. By leveraging Gaussian process regression and imposing the governing PDE as a constraint at a finite set of collocation…

  • Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps

    Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps arXiv:2506.02254v1 Announce Type: new Abstract: We present a generative learning framework for probabilistic sampling based on an extension of the Probabilistic Learning on Manifolds (PLoM) approach, which is designed to generate statistically consistent realizations of a random vector in a finite-dimensional Euclidean space, informed by a…

  • Fixing the Pitfalls of Probabilistic Time-Series Forecasting Evaluation by Kernel Quadrature

    Fixing the Pitfalls of Probabilistic Time-Series Forecasting Evaluation by Kernel Quadrature arXiv:2503.06079v1 Announce Type: new Abstract: Despite the significance of probabilistic time-series forecasting models, their evaluation metrics often involve intractable integrations. The most widely used metric, the continuous ranked probability score (CRPS), is a strictly proper scoring function; however, its computation requires approximation. We found…