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Tag: statistical

  • Statistical Inference for Explainable Boosting Machines

    Statistical Inference for Explainable Boosting Machines arXiv:2601.18857v1 Announce Type: new Abstract: Explainable boosting machines (EBMs) are popular “glass-box” models that learn a set of univariate functions using boosting trees. These achieve explainability through visualizations of each feature’s effect. However, unlike linear model coefficients, uncertainty quantification for the learned univariate functions requires computationally intensive bootstrapping, making…

  • Categorical and geometric methods in statistical, manifold, and machine learning

    Categorical and geometric methods in statistical, manifold, and machine learning arXiv:2505.03862v1 Announce Type: new Abstract: We present and discuss applications of the category of probabilistic morphisms, initially developed in cite{Le2023}, as well as some geometric methods to several classes of problems in statistical, machine and manifold learning which shall be, along with many other topics,…

  • Post-Transfer Learning Statistical Inference in High-Dimensional Regression

    Post-Transfer Learning Statistical Inference in High-Dimensional Regression arXiv:2504.18212v1 Announce Type: new Abstract: Transfer learning (TL) for high-dimensional regression (HDR) is an important problem in machine learning, particularly when dealing with limited sample size in the target task. However, there currently lacks a method to quantify the statistical significance of the relationship between features and the…

  • Statistical Inference in Reinforcement Learning: A Selective Survey

    Statistical Inference in Reinforcement Learning: A Selective Survey arXiv:2502.16195v1 Announce Type: new Abstract: Reinforcement learning (RL) is concerned with how intelligence agents take actions in a given environment to maximize the cumulative reward they receive. In healthcare, applying RL algorithms could assist patients in improving their health status. In ride-sharing platforms, applying RL algorithms could…

  • On the use of Statistical Learning Theory for model selection in Structural Health Monitoring

    On the use of Statistical Learning Theory for model selection in Structural Health Monitoring arXiv:2501.08050v1 Announce Type: new Abstract: Whenever data-based systems are employed in engineering applications, defining an optimal statistical representation is subject to the problem of model selection. This paper focusses on how well models can generalise in Structural Health Monitoring (SHM). Although…

  • Computational and Statistical Asymptotic Analysis of the JKO Scheme for Iterative Algorithms to update distributions

    Computational and Statistical Asymptotic Analysis of the JKO Scheme for Iterative Algorithms to update distributions arXiv:2501.06408v1 Announce Type: new Abstract: The seminal paper of Jordan, Kinderlehrer, and Otto introduced what is now widely known as the JKO scheme, an iterative algorithmic framework for computing distributions. This scheme can be interpreted as a Wasserstein gradient flow…

  • Four Ways to Improve Statistical Power in A/B Testing (Without Increasing Test Duration, Duh)

    Four Ways to Improve Statistical Power in A/B Testing (Without Increasing Test Duration, Duh) In A/B testing, you often have to balance statistical power and how long the test takes. Learn how Allocation, Effect Size, CUPED & Binarization can help you. Image by author In A/B testing, you often have to balance statistical power and how long…

  • In Defense of Statistical Significance

    In Defense of Statistical Significance We have to draw the line somewhere Photo by Siora Photography on Unsplash It’s become something of a meme that statistical significance is a bad standard. Several recent blogs have made the rounds, making the case that statistical significance is a “cult” or “arbitrary.” If you’d like a classic polemic (and…

  • Jingle Bells and Statistical Tests

    Jingle Bells and Statistical Tests Data Types, Hypotheses and Statistical Tests That Fit Them with Festive Christmas Market Examples🎄🎅🎡 Continue reading on Towards Data Science » Gizem Kaya Go to original source

  • A Statistical Framework for Ranking LLM-Based Chatbots

    A Statistical Framework for Ranking LLM-Based Chatbots arXiv:2412.18407v1 Announce Type: new Abstract: Large language models (LLMs) have transformed natural language processing, with frameworks like Chatbot Arena providing pioneering platforms for evaluating these models. By facilitating millions of pairwise comparisons based on human judgments, Chatbot Arena has become a cornerstone in LLM evaluation, offering rich datasets…

  • Statistical Undersampling with Mutual Information and Support Points

    Statistical Undersampling with Mutual Information and Support Points arXiv:2412.14527v1 Announce Type: new Abstract: Class imbalance and distributional differences in large datasets present significant challenges for classification tasks machine learning, often leading to biased models and poor predictive performance for minority classes. This work introduces two novel undersampling approaches: mutual information-based stratified simple random sampling and…

  • Why “Statistical Significance” Is Pointless

    Why “Statistical Significance” Is Pointless Here’s a better framework for data-driven decision-making Continue reading on Towards Data Science » Samuele Mazzanti Go to original source