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Importance sampling (IS) is an efficient stand-in for model refitting in performing (LOO) cross-validation (CV) on a Bayesian model.
Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to reduce preventable all-cause readmissions or death
Ted L. Chang, Hongjing Xia, Sonya Mahajan, Rohit Mahajan, Joe Maisog, Shashaank Vattikuti, Carson C. Chow, and Joshua C. Chang · 1932
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Sparsity information and regularization in the horseshoe and other shrinkage priors
Juho Piironen and Aki Vehtari · 1935
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An Asymptotic Equivalence of Choice of Model by Cross-Validation and Akaike’s Criterion
M. Stone · 1977
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Model determination using predictive distributions with implementation via sampling-based methods
Alan E. Gelfand, Dipak K. Dey, and Hong Chang · 1992
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A study of cross-validation and bootstrap for accuracy estimation and model selection
Ron Kohavi · 1995
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On the Variability of Case-Deletion Importance Sampling Weights in the Bayesian Linear Model
Mario Peruggia · 1997
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Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms
T. G. Dietterich · 1998
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Comparative hybridization of an array of 21 500 ovarian cDNAs for the discovery of genes overexpressed in ovarian carcinomas
Michèl Schummer, WaiLap V Ng, Roger E Bumgarner, Peter S Nelson, Bernhard Schummer, David W Bednarski, Laurie Hassell, Rae Lynn Baldwin, Beth Y Karlan, and Leroy Hood · 1999
Earlier work this paper cites.
Consistency of posterior distributions for neural networks
H. K. Lee · 2000
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Truncated Importance Sampling
Edward L. Ionides · 2008
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Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation
J.D. Rodriguez, A. Perez, and J.A. Lozano · 2009
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A New and Efficient Estimation Method for the Generalized Pareto Distribution
Jin Zhang and Michael A. Stephens · 2009
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Handling Sparsity via the Horseshoe
Carlos M. Carvalho, Nicholas G. Polson, and James G. Scott · 2009
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Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory
Sumio Watanabe · 2010
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Expectation Propagation for microarray data classification
Daniel Hernández-Lobato, José Miguel Hernández-Lobato, and Alberto Suárez · 2010
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Adaptive Multiple Importance Sampling, October 2011
Jean-Marie Cornuet, Jean-Michel Marin, Antonietta Mira, and Christian P. Robert · 2011
Cited alongside, same era.
A Widely Applicable Bayesian Information Criterion
Sumio Watanabe · 2013
Cited alongside, same era.
Monte Carlo Statistical Methods
Christian Robert and George Casella · 2013
Cited alongside, same era.
Understanding predictive information criteria for Bayesian models
Andrew Gelman, Jessica Hwang, and Aki Vehtari · 2014
Cited alongside, same era.
On the Number of Linear Regions of Deep Neural Networks, June 2014
Guido Montúfar, Razvan Pascanu, Kyunghyun Cho, and Yoshua Bengio · 2014
Cited alongside, same era.
A gradient adaptive population importance sampler
Víctor Elvira, Luca Martino, David Luengo, and Jukka Corander · 2015
Cited alongside, same era.
Reliable Accuracy Estimates from k -Fold Cross Validation
Tzu-Tsung Wong and Po-Yang Yeh · 2019
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Horseshoe Regularization for Machine Learning in Complex and Deep Models
Anindya Bhadra, Jyotishka Datta, Yunfan Li, and Nicholas G. Polson · 2019
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Monte Carlo Methods
Adrian Barbu and Song-Chun Zhu · 2020
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Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks, July 2020
Agustinus Kristiadi, Matthias Hein, and Philipp Hennig · 2020
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Unwrapping The Black Box of Deep ReLU Networks: Interpretability, Diagnostics, and Simplification
Agus Sudjianto, William Knauth, Rahul Singh, Zebin Yang, and Aijun Zhang · 2020
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Implicitly adaptive importance sampling
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Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
Aki Vehtari, Andrew Gelman, and Jonah Gabry · 2017
Cited alongside, same era.
Adaptive Importance Sampling: The past, the present, and the future
Monica F. Bugallo, Victor Elvira, Luca Martino, David Luengo, Joaquin Miguez, and Petar M. Djuric · 2017
Cited alongside, same era.
Automatic differentiation variational inference
Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, and David M. Blei · 2017
Cited alongside, same era.
Variational Inference: A Review for Statisticians
David M. Blei, Alp Kucukelbir, and Jon D. McAuliffe · 2017
Cited alongside, same era.
Brms: An R Package for Bayesian Multilevel Models Using Stan
Paul-Christian Bürkner · 2017
Cited alongside, same era.
Model Selection in Bayesian Neural Networks via Horseshoe Priors
Soumya Ghosh and Finale Doshi-Velez · 2017
Cited alongside, same era.
Topi Paananen, Juho Piironen, Paul-Christian Bürkner, and Aki Vehtari · 2021
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Linear Iterative Feature Embedding: An Ensemble Framework for Interpretable Model
Agus Sudjianto, Jinwen Qiu, Miaoqi Li, and Jie Chen · 2021
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Advances in Importance Sampling, March 2022
Víctor Elvira and Luca Martino · 2022
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Gradient-based Adaptive Importance Samplers
Víctor Elvira, Emilie Chouzenoux, Ömer Deniz Akyildiz, and Luca Martino · 2022
Later among the works it cites.
Estimation and Comparison of Linear Regions for ReLU Networks
Yuan Wang · 2022
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Robust leave-one-out cross-validation for high-dimensional Bayesian models
Luca Silva and Giacomo Zanella · 2023
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Interpretable (not just posthoc-explainable) heterogeneous survivors bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions
Hongjing Xia, Joshua C. Chang, Sarah Nowak, Sonya Mahajan, Rohit Mahajan, Ted L. Chang, and Carson C. Chow · 2023
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Pareto Smoothed Importance Sampling
Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, and Jonah Gabry · 2024
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Randomized 2×2 trial evaluating hormonal treatment and the duration of chemotherapy in node-positive breast cancer patients
M. Schumacher, G. Bastert, H. Bojar, K. Hübner, M. Olschewski, W. Sauerbrei, C. Schmoor, C. Beyerle, R. L. A. Neumann, and H. F. Rauschecker · 2086
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