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Tag: function

  • Partition Function Estimation under Bounded f-Divergence

    Partition Function Estimation under Bounded f-Divergence arXiv:2602.23535v1 Announce Type: new Abstract: We study the statistical complexity of estimating partition functions given sample access to a proposal distribution and an unnormalized density ratio for a target distribution. While partition function estimation is a classical problem, existing guarantees typically rely on structural assumptions about the domain or…

  • YOLOv1 Loss Function Walkthrough: Regression for All

    YOLOv1 Loss Function Walkthrough: Regression for All An explanation of how YOLOv1 measures the correctness of its object detection and classification predictions The post YOLOv1 Loss Function Walkthrough: Regression for All appeared first on Towards Data Science. Muhammad Ardi Go to original source

  • 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…

  • A Dual Optimization View to Empirical Risk Minimization with f-Divergence Regularization

    A Dual Optimization View to Empirical Risk Minimization with f-Divergence Regularization arXiv:2508.03314v1 Announce Type: new Abstract: The dual formulation of empirical risk minimization with f-divergence regularization (ERM-fDR) is introduced. The solution of the dual optimization problem to the ERM-fDR is connected to the notion of normalization function introduced as an implicit function. This dual approach…

  • Optimal Convergence Rates of Deep Neural Network Classifiers

    Optimal Convergence Rates of Deep Neural Network Classifiers arXiv:2506.14899v1 Announce Type: new Abstract: In this paper, we study the binary classification problem on $[0,1]^d$ under the Tsybakov noise condition (with exponent $s in [0,infty]$) and the compositional assumption. This assumption requires the conditional class probability function of the data distribution to be the composition of…

  • Fast Bayesian Optimization of Function Networks with Partial Evaluations

    Fast Bayesian Optimization of Function Networks with Partial Evaluations arXiv:2506.11456v1 Announce Type: new Abstract: Bayesian optimization of function networks (BOFN) is a framework for optimizing expensive-to-evaluate objective functions structured as networks, where some nodes’ outputs serve as inputs for others. Many real-world applications, such as manufacturing and drug discovery, involve function networks with additional properties…

  • Self-Consistent Equation-guided Neural Networks for Censored Time-to-Event Data

    Self-Consistent Equation-guided Neural Networks for Censored Time-to-Event Data arXiv:2503.09097v1 Announce Type: new Abstract: In survival analysis, estimating the conditional survival function given predictors is often of interest. There is a growing trend in the development of deep learning methods for analyzing censored time-to-event data, especially when dealing with high-dimensional predictors that are complexly interrelated. Many…

  • A Derivation and Application of Restricted Boltzmann Machines (2024 Nobel Prize)

    A Derivation and Application of Restricted Boltzmann Machines (2024 Nobel Prize) Investigating Geoffrey Hinton’s Nobel Prize-winning work and building it from scratch using PyTorch One recipient of the 2024 Nobel Prize in Physics was Geoffrey Hinton for his contributions in the field of AI and machine learning. A lot of people know he worked on neural…

  • Globally Convergent Variational Inference

    Globally Convergent Variational Inference arXiv:2501.08201v1 Announce Type: new Abstract: In variational inference (VI), an approximation of the posterior distribution is selected from a family of distributions through numerical optimization. With the most common variational objective function, known as the evidence lower bound (ELBO), only convergence to a local optimum can be guaranteed. In this work,…