Probit Monotone BART

• URL: http://arxiv.org/abs/2509.00263v1
• Date: Fri, 29 Aug 2025 22:19:02 GMT
• Title: Probit Monotone BART
• Authors: Jared D. Fisher,
• Abstract summary: We propose probit monotone BART, which allows the monotone BART framework to estimate conditional mean functions when the outcome variable is binary.<n>BART of Chipman et al. (2010) has proven to be a powerful tool for nonparametric modeling and prediction.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bayesian Additive Regression Trees (BART) of Chipman et al. (2010) has proven to be a powerful tool for nonparametric modeling and prediction. Monotone BART (Chipman et al., 2022) is a recent development that allows BART to be more precise in estimating monotonic functions. We further these developments by proposing probit monotone BART, which allows the monotone BART framework to estimate conditional mean functions when the outcome variable is binary.

Related papers

• Bayesian Additive Regression Trees for functional ANOVA model [13.402140652721618]
  ANOVA Bayesian Additive Regression Trees (ANOVA-BART) is a novel extension of Bayesian Additive Regression Trees (BART)<n>Our proposed ANOVA-BART enhances interpretability, preserves and extends the theoretical guarantees of BART, and achieves superior predictive performance.<n>These results suggest that ANOVA-BART offers a compelling alternative to BART by balancing predictive accuracy, interpretability, and theoretical consistency.
  arXiv  Detail & Related papers  (2025-09-03T13:50:45Z)
• On the Gaussian process limit of Bayesian Additive Regression Trees [0.0]
  In the limit of infinite trees, BART becomes equivalent to Gaussian process (GP) regression.<n>This study opens new ways to understand and develop BART and GP regression.<n>The implementation of BART as GP is available in the Python package lsqfitgp.
  arXiv  Detail & Related papers  (2024-10-26T23:18:33Z)
• ASBART:Accelerated Soft Bayes Additive Regression Trees [8.476756500467689]
  Soft BART improves both practically and heoretically on existing Bayesian sum-of-trees models. Compared to BART,it use more than about 20 times to complete the calculation with the default setting. We proposed a variant of BART named accelerate Soft BART(ASBART)
  arXiv  Detail & Related papers  (2023-10-21T11:27:42Z)
• Contextual Combinatorial Bandits with Probabilistically Triggered Arms [55.9237004478033]
  We study contextual bandits with probabilistically triggered arms (C$2$MAB-T) under a variety of smoothness conditions. Under the triggering modulated (TPM) condition, we devise the C$2$-UC-T algorithm and derive a regret bound $tildeO(dsqrtT)$.
  arXiv  Detail & Related papers  (2023-03-30T02:51:00Z)
• SoftBart: Soft Bayesian Additive Regression Trees [2.969705152497174]
  This paper introduces the SoftBart package for fitting the Soft BART algorithm of Linero and Yang. A major goal of this package has been to facilitate the inclusion of BART in larger models. I show both how to use this package for standard prediction tasks and how to embed BART models in larger models.
  arXiv  Detail & Related papers  (2022-10-28T19:25:45Z)
• A Mixing Time Lower Bound for a Simplified Version of BART [5.149859291357858]
  We provide the first lower bound on the mixing time for a simplified version of BART. Inspired by this new connection between the mixing time and the number of data points, we perform rigorous simulations on BART. We show qualitatively that BART's mixing time increases with the number of data points.
  arXiv  Detail & Related papers  (2022-10-17T18:45:36Z)
• Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error [83.10489974736404]
  We study the use of the Bellman equation as a surrogate objective for value prediction accuracy. We find that the Bellman error is a poor proxy for the accuracy of the value function.
  arXiv  Detail & Related papers  (2022-01-28T21:03:59Z)
• Bayesian Bellman Operators [55.959376449737405]
  We introduce a novel perspective on Bayesian reinforcement learning (RL) Our framework is motivated by the insight that when bootstrapping is introduced, model-free approaches actually infer a posterior over Bellman operators, not value functions.
  arXiv  Detail & Related papers  (2021-06-09T12:20:46Z)
• Double Forward Propagation for Memorized Batch Normalization [68.34268180871416]
  Batch Normalization (BN) has been a standard component in designing deep neural networks (DNNs) We propose a memorized batch normalization (MBN) which considers multiple recent batches to obtain more accurate and robust statistics. Compared to related methods, the proposed MBN exhibits consistent behaviors in both training and inference.
  arXiv  Detail & Related papers  (2020-10-10T08:48:41Z)
• KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning [78.81080813406177]
  We propose a novel knowledge graph augmented pre-trained language generation model KG-BART. KG-BART encompasses the complex relations of concepts through the knowledge graph and produces more logical and natural sentences as output.
  arXiv  Detail & Related papers  (2020-09-26T19:57:49Z)
• Learning Near Optimal Policies with Low Inherent Bellman Error [115.16037976819331]
  We study the exploration problem with approximate linear action-value functions in episodic reinforcement learning. We show that exploration is possible using only emphbatch assumptions with an algorithm that achieves the optimal statistical rate for the setting we consider.
  arXiv  Detail & Related papers  (2020-02-29T02:02:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.