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Tag: functions

  • Plug-In Classification of Drift Functions in Diffusion Processes Using Neural Networks

    Plug-In Classification of Drift Functions in Diffusion Processes Using Neural Networks arXiv:2602.02791v1 Announce Type: new Abstract: We study a supervised multiclass classification problem for diffusion processes, where each class is characterized by a distinct drift function and trajectories are observed at discrete times. Extending the one-dimensional multiclass framework of Denis et al. (2024) to multidimensional…

  • Global Optimization of Stochastic Black-Box Functions with Arbitrary Noise Distributions using Wilson Score Kernel Density Estimation

    Global Optimization of Stochastic Black-Box Functions with Arbitrary Noise Distributions using Wilson Score Kernel Density Estimation arXiv:2509.09238v1 Announce Type: new Abstract: Many optimization problems in robotics involve the optimization of time-expensive black-box functions, such as those involving complex simulations or evaluation of real-world experiments. Furthermore, these functions are often stochastic as repeated experiments are subject…

  • Sinusoidal Approximation Theorem for Kolmogorov-Arnold Networks

    Sinusoidal Approximation Theorem for Kolmogorov-Arnold Networks arXiv:2508.00247v1 Announce Type: new Abstract: The Kolmogorov-Arnold representation theorem states that any continuous multivariable function can be exactly represented as a finite superposition of continuous single variable functions. Subsequent simplifications of this representation involve expressing these functions as parameterized sums of a smaller number of unique monotonic functions. These…

  • Mastering SQL Window Functions

    Mastering SQL Window Functions Understand how to use Window Functions to perform calculations without losing details The post Mastering SQL Window Functions appeared first on Towards Data Science. Eugenia Anello Go to original source

  • Optimizing Multi-Objective Problems with Desirability Functions

    Optimizing Multi-Objective Problems with Desirability Functions When working in Data Science, it is not uncommon to encounter problems with competing objectives. Whether designing products, tuning algorithms or optimizing portfolios, we often need to balance several metrics to get the best possible outcome. Sometimes, maximizing one metrics comes at the expense of another, making it hard…

  • Two in context learning tasks with complex functions

    Two in context learning tasks with complex functions arXiv:2502.03503v1 Announce Type: new Abstract: We examine two in context learning (ICL) tasks with mathematical functions in several train and test settings for transformer models. Our study generalizes work on linear functions by showing that small transformers, even models with attention layers only, can approximate arbitrary polynomial…

  • A Unified Framework for Entropy Search and Expected Improvement in Bayesian Optimization

    A Unified Framework for Entropy Search and Expected Improvement in Bayesian Optimization arXiv:2501.18756v1 Announce Type: new Abstract: Bayesian optimization is a widely used method for optimizing expensive black-box functions, with Expected Improvement being one of the most commonly used acquisition functions. In contrast, information-theoretic acquisition functions aim to reduce uncertainty about the function’s optimum and…

  • Extension of Symmetrized Neural Network Operators with Fractional and Mixed Activation Functions

    Extension of Symmetrized Neural Network Operators with Fractional and Mixed Activation Functions arXiv:2501.10496v1 Announce Type: new Abstract: We propose a novel extension to symmetrized neural network operators by incorporating fractional and mixed activation functions. This study addresses the limitations of existing models in approximating higher-order smooth functions, particularly in complex and high-dimensional spaces. Our framework…