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Tag: box

  • The Black Box Problem: Why AI-Generated Code Stops Being Maintainable

    The Black Box Problem: Why AI-Generated Code Stops Being Maintainable Same notification system, two architectures. Unstructured generation couples everything into a single module. Structured generation decomposes into independent components with explicit, one-directional dependencies. Image by the author The post The Black Box Problem: Why AI-Generated Code Stops Being Maintainable appeared first on Towards Data Science.…

  • Efficient optimization of expensive black-box simulators via marginal means, with application to neutrino detector design

    Efficient optimization of expensive black-box simulators via marginal means, with application to neutrino detector design arXiv:2508.01834v1 Announce Type: new Abstract: With advances in scientific computing, computer experiments are increasingly used for optimizing complex systems. However, for modern applications, e.g., the optimization of nuclear physics detectors, each experiment run can require hundreds of CPU hours, making…

  • AI Is Not a Black Box (Relatively Speaking)

    AI Is Not a Black Box (Relatively Speaking) Compared to the opacity around human intelligence, AI is more transparent in some very tangible ways. The post AI Is Not a Black Box (Relatively Speaking) appeared first on Towards Data Science. Piotr (Peter) Mardziel Go to original source

  • Box-Constrained Softmax Function and Its Application for Post-Hoc Calibration

    Box-Constrained Softmax Function and Its Application for Post-Hoc Calibration arXiv:2506.10572v1 Announce Type: new Abstract: Controlling the output probabilities of softmax-based models is a common problem in modern machine learning. Although the $mathrm{Softmax}$ function provides soft control via its temperature parameter, it lacks the ability to enforce hard constraints, such as box constraints, on output probabilities,…

  • Multi-Objective Bayesian Optimization for Networked Black-Box Systems: A Path to Greener Profits and Smarter Designs

    Multi-Objective Bayesian Optimization for Networked Black-Box Systems: A Path to Greener Profits and Smarter Designs arXiv:2502.14121v1 Announce Type: new Abstract: Designing modern industrial systems requires balancing several competing objectives, such as profitability, resilience, and sustainability, while accounting for complex interactions between technological, economic, and environmental factors. Multi-objective optimization (MOO) methods are commonly used to navigate…

  • Leveraging Black-box Models to Assess Feature Importance in Unconditional Distribution

    Leveraging Black-box Models to Assess Feature Importance in Unconditional Distribution arXiv:2412.05759v1 Announce Type: new Abstract: Understanding how changes in explanatory features affect the unconditional distribution of the outcome is important in many applications. However, existing black-box predictive models are not readily suited for analyzing such questions. In this work, we develop an approximation method to…