mailitics

Tag: boosting

  • 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…

  • Boosting methods for interval-censored data with regression and classification

    Boosting methods for interval-censored data with regression and classification arXiv:2601.17973v1 Announce Type: new Abstract: Boosting has garnered significant interest across both machine learning and statistical communities. Traditional boosting algorithms, designed for fully observed random samples, often struggle with real-world problems, particularly with interval-censored data. This type of data is common in survival analysis and time-to-event…

  • Boosting Your Anomaly Detection With LLMs

    Boosting Your Anomaly Detection With LLMs The 7 emerging application patterns you should know The post Boosting Your Anomaly Detection With LLMs appeared first on Towards Data Science. Shuai Guo Go to original source

  • Subgrid BoostCNN: Efficient Boosting of Convolutional Networks via Gradient-Guided Feature Selection

    Subgrid BoostCNN: Efficient Boosting of Convolutional Networks via Gradient-Guided Feature Selection arXiv:2507.22842v1 Announce Type: new Abstract: Convolutional Neural Networks (CNNs) have achieved remarkable success across a wide range of machine learning tasks by leveraging hierarchical feature learning through deep architectures. However, the large number of layers and millions of parameters often make CNNs computationally expensive…

  • Random Feature Representation Boosting

    Random Feature Representation Boosting arXiv:2501.18283v1 Announce Type: new Abstract: We introduce Random Feature Representation Boosting (RFRBoost), a novel method for constructing deep residual random feature neural networks (RFNNs) using boosting theory. RFRBoost uses random features at each layer to learn the functional gradient of the network representation, enhancing performance while preserving the convex optimization benefits…

  • LightGBM: The Fastest Option of Gradient Boosting

    LightGBM: The Fastest Option of Gradient Boosting Learn how to implement a fast and effective Gradient Boosting model using Python Continue reading on Towards Data Science ยป Gustavo R Santos Go to original source

  • From Point to probabilistic gradient boosting for claim frequency and severity prediction

    From Point to probabilistic gradient boosting for claim frequency and severity prediction arXiv:2412.14916v1 Announce Type: new Abstract: Gradient boosting for decision tree algorithms are increasingly used in actuarial applications as they show superior predictive performance over traditional generalized linear models. Many improvements and sophistications to the first gradient boosting machine algorithm exist. We present in…