paper-with-me

홈 › Papers

ASBART:Accelerated Soft Bayes Additive Regression Trees

2023-10-21 · Hao Ran, Yang Bai

Bayes additive regression trees(BART) is a nonparametric regression model which has gained wide-spread popularity in recent years due to its flexibility and high accuracy of estimation. Soft BART,one variation of BART,improves both practically and heoretically on existing Bayesian sum-of-trees models. One bottleneck for Soft BART is its slow speed in the long MCMC loop. 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). Simulation studies show that the new method is about 10 times faster than the Soft BART with comparable accuracy. Our code is open-source and available at https://github.com/richael008/XSBART.

📄 PDF Abstract BibTeX arXiv:2310.13975

Code (0)

등록된 구현이 없습니다.

Tasks

regression

Methods 이 논문이 사용한 방법론

Multi-Head Attention 설명 없음
Attention 설명 없음
Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…
Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…
Adam 설명 없음
Residual Connection 설명 없음
BPE Byte Pair Encoding, or BPE, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word…
Dropout Dropout is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is…

Similar Papers 제목 키워드 기반

SoftBart: Soft Bayesian Additive Regression Trees

2022-10-28 · Antonio R. Linero

Bayesian additive regression tree (BART) models have seen increased attention in recent years as a general-purpose nonparametric modeling technique. BART combines the flexibility of modern machine learning techniques wit…

Bayesian InferenceregressionUncertainty Quantification

XBART: Accelerated Bayesian Additive Regression Trees

2018-10-04 · Jingyu He, Saar Yalov, P. Richard Hahn

Bayesian additive regression trees (BART) (Chipman et. al., 2010) is a powerful predictive model that often outperforms alternative models at out-of-sample prediction. BART is especially well-suited to settings with unst…

regression

Stochastic tree ensembles for regularized nonlinear regression

2020-02-09 · Jingyu He, P. Richard Hahn

This paper develops a novel stochastic tree ensemble method for nonlinear regression, which we refer to as XBART, short for Accelerated Bayesian Additive Regression Trees. By combining regularization and stochastic searc…

regression

Sparse additive Gaussian process with soft interactions

2016-07-09 · Garret Vo, Debdeep Pati

Additive nonparametric regression models provide an attractive tool for variable selection in high dimensions when the relationship between the response and predictors is complex. They offer greater flexibility compared …

regressionVariable Selection

bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond)

2019-09-25 · Nikolaus Umlauf, Nadja Klein, Thorsten Simon, Achim Zeileis

Over the last decades, the challenges in applied regression and in predictive modeling have been changing considerably: (1) More flexible model specifications are needed as big(ger) data become available, facilitated by …

Additive modelsBayesian Inferenceregression