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Tag: additive

  • Sparse Additive Model Pruning for Order-Based Causal Structure Learning

    Sparse Additive Model Pruning for Order-Based Causal Structure Learning arXiv:2602.15306v1 Announce Type: new Abstract: Causal structure learning, also known as causal discovery, aims to estimate causal relationships between variables as a form of a causal directed acyclic graph (DAG) from observational data. One of the major frameworks is the order-based approach that first estimates a…

  • On Thompson Sampling and Bilateral Uncertainty in Additive Bayesian Optimization

    On Thompson Sampling and Bilateral Uncertainty in Additive Bayesian Optimization arXiv:2510.11792v1 Announce Type: new Abstract: In Bayesian Optimization (BO), additive assumptions can mitigate the twin difficulties of modeling and searching a complex function in high dimension. However, common acquisition functions, like the Additive Lower Confidence Bound, ignore pairwise covariances between dimensions, which we’ll call textit{bilateral…

  • Bayesian Additive Regression Trees for functional ANOVA model

    Bayesian Additive Regression Trees for functional ANOVA model arXiv:2509.03317v1 Announce Type: new Abstract: Bayesian Additive Regression Trees (BART) is a powerful statistical model that leverages the strengths of Bayesian inference and regression trees. It has received significant attention for capturing complex non-linear relationships and interactions among predictors. However, the accuracy of BART often comes at…

  • Gradient-free stochastic optimization for additive models

    Gradient-free stochastic optimization for additive models arXiv:2503.02131v1 Announce Type: new Abstract: We address the problem of zero-order optimization from noisy observations for an objective function satisfying the Polyak-{L}ojasiewicz or the strong convexity condition. Additionally, we assume that the objective function has an additive structure and satisfies a higher-order smoothness property, characterized by the H”older family…

  • SBAMDT: Bayesian Additive Decision Trees with Adaptive Soft Semi-multivariate Split Rules

    SBAMDT: Bayesian Additive Decision Trees with Adaptive Soft Semi-multivariate Split Rules arXiv:2501.09900v1 Announce Type: new Abstract: Bayesian Additive Regression Trees [BART, Chipman et al., 2010] have gained significant popularity due to their remarkable predictive performance and ability to quantify uncertainty. However, standard decision tree models rely on recursive data splits at each decision node, using…