Stabilizing black-box model selection with the inflated argmax
Fuente:
arXiv
Saved in:
| Main Authors: | Adrian, Melissa, Soloff, Jake A., Willett, Rebecca |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Building a stable classifier with the inflated argmax
by: Soloff, Jake A., et al.
Published: (2024)
by: Soloff, Jake A., et al.
Published: (2024)
Assumption-free stability for ranking problems
by: Liang, Ruiting, et al.
Published: (2025)
by: Liang, Ruiting, et al.
Published: (2025)
Can a calibration metric be both testable and actionable?
by: Rossellini, Raphael, et al.
Published: (2025)
by: Rossellini, Raphael, et al.
Published: (2025)
Bagging Provides Assumption-free Stability
by: Soloff, Jake A., et al.
Published: (2023)
by: Soloff, Jake A., et al.
Published: (2023)
Personalizing black-box models for nonparametric regression with minimax optimality
by: Li, Sai, et al.
Published: (2026)
by: Li, Sai, et al.
Published: (2026)
Integrating Uncertainty Awareness into Conformalized Quantile Regression
by: Rossellini, Raphael, et al.
Published: (2023)
by: Rossellini, Raphael, et al.
Published: (2023)
Incentive-Theoretic Bayesian Inference for Collaborative Science
by: Bates, Stephen, et al.
Published: (2023)
by: Bates, Stephen, et al.
Published: (2023)
Principal-Agent Hypothesis Testing
by: Bates, Stephen, et al.
Published: (2022)
by: Bates, Stephen, et al.
Published: (2022)
Efficient optimization of expensive black-box simulators via marginal means, with application to neutrino detector design
by: Kim, Hwanwoo, et al.
Published: (2025)
by: Kim, Hwanwoo, et al.
Published: (2025)
When the whole is greater than the sum of its parts: Scaling black-box inference to large data settings through divide-and-conquer
by: Hector, Emily C., et al.
Published: (2024)
by: Hector, Emily C., et al.
Published: (2024)
What is in the model? A Comparison of variable selection criteria and model search approaches
by: Xu, Shuangshuang, et al.
Published: (2025)
by: Xu, Shuangshuang, et al.
Published: (2025)
Dendrogram of mixing measures: Hierarchical clustering and model selection for finite mixture models
by: Do, Dat, et al.
Published: (2024)
by: Do, Dat, et al.
Published: (2024)
Improving Active Learning with a Bayesian Representation of Epistemic Uncertainty
by: Thomas, Jake, et al.
Published: (2024)
by: Thomas, Jake, et al.
Published: (2024)
A model-free subdata selection method for classification
by: Singh, Rakhi
Published: (2024)
by: Singh, Rakhi
Published: (2024)
Dirichlet process mixtures of block $g$ priors for model selection and prediction in linear models
by: Porwal, Anupreet, et al.
Published: (2024)
by: Porwal, Anupreet, et al.
Published: (2024)
Group selection and shrinkage: Structured sparsity for semiparametric additive models
by: Thompson, Ryan, et al.
Published: (2021)
by: Thompson, Ryan, et al.
Published: (2021)
A Framework for Improving the Reliability of Black-box Variational Inference
by: Welandawe, Manushi, et al.
Published: (2022)
by: Welandawe, Manushi, et al.
Published: (2022)
Cross-Validation with Antithetic Gaussian Randomization
by: Liu, Sifan, et al.
Published: (2024)
by: Liu, Sifan, et al.
Published: (2024)
Zero-inflation in the Multivariate Poisson Lognormal Family
by: Batardière, Bastien, et al.
Published: (2024)
by: Batardière, Bastien, et al.
Published: (2024)
Knowledge Distillation Decision Tree for Unravelling Black-box Machine Learning Models
by: Lu, Xuetao, et al.
Published: (2022)
by: Lu, Xuetao, et al.
Published: (2022)
Stability via resampling: statistical problems beyond the real line
by: Soloff, Jake A., et al.
Published: (2024)
by: Soloff, Jake A., et al.
Published: (2024)
Integrated path stability selection
by: Melikechi, Omar, et al.
Published: (2024)
by: Melikechi, Omar, et al.
Published: (2024)
Leveraging Black-box Models to Assess Feature Importance in Unconditional Distribution
by: Zhou, Jing, et al.
Published: (2024)
by: Zhou, Jing, et al.
Published: (2024)
ACS: An interactive framework for conformal selection
by: Gui, Yu, et al.
Published: (2025)
by: Gui, Yu, et al.
Published: (2025)
Flexible variable selection in the presence of missing data
by: Williamson, B. D., et al.
Published: (2022)
by: Williamson, B. D., et al.
Published: (2022)
Guiding adaptive shrinkage by co-data to improve regression-based prediction and feature selection
by: van de Wiel, Mark A., et al.
Published: (2024)
by: van de Wiel, Mark A., et al.
Published: (2024)
A Dirichlet stochastic block model for composition-weighted networks
by: Promskaia, Iuliia, et al.
Published: (2024)
by: Promskaia, Iuliia, et al.
Published: (2024)
Assessing Surrogate Heterogeneity in Real World Data Using Meta-Learners
by: Knowlton, Rebecca, et al.
Published: (2025)
by: Knowlton, Rebecca, et al.
Published: (2025)
Sparse-group boosting -- Unbiased group and variable selection
by: Obster, Fabian, et al.
Published: (2022)
by: Obster, Fabian, et al.
Published: (2022)
Sparse minimum Redundancy Maximum Relevance for feature selection
by: Naylor, Peter, et al.
Published: (2025)
by: Naylor, Peter, et al.
Published: (2025)
Neural interval-censored survival regression with feature selection
by: Meixide, Carlos García, et al.
Published: (2022)
by: Meixide, Carlos García, et al.
Published: (2022)
Bayesian model-averaging stochastic item selection for adaptive testing
by: Su, Tina, et al.
Published: (2025)
by: Su, Tina, et al.
Published: (2025)
Multiplex Dirichlet stochastic block model for clustering multidimensional compositional networks
by: Promskaia, Iuliia, et al.
Published: (2024)
by: Promskaia, Iuliia, et al.
Published: (2024)
PCS-UQ: Uncertainty Quantification via the Predictability-Computability-Stability Framework
by: Agarwal, Abhineet, et al.
Published: (2025)
by: Agarwal, Abhineet, et al.
Published: (2025)
PliableBVS: A flexible Bayesian variable selection method for modeling interactions with mandatory modifying variables
by: Asenso, Theophilus Quachie, et al.
Published: (2026)
by: Asenso, Theophilus Quachie, et al.
Published: (2026)
Autotune: fast, accurate, and automatic tuning parameter selection for Lasso
by: Sadhukhan, Tathagata, et al.
Published: (2025)
by: Sadhukhan, Tathagata, et al.
Published: (2025)
Stability of clinical prediction models developed using statistical or machine learning methods
by: Riley, Richard D, et al.
Published: (2022)
by: Riley, Richard D, et al.
Published: (2022)
The Hardness of Validating Observational Studies with Experimental Data
by: Fawkes, Jake, et al.
Published: (2025)
by: Fawkes, Jake, et al.
Published: (2025)
Online detection of forecast model inadequacies using forecast errors
by: Grundy, Thomas, et al.
Published: (2025)
by: Grundy, Thomas, et al.
Published: (2025)
VICatMix: variational Bayesian clustering and variable selection for discrete biomedical data
by: Rao, Jackie, et al.
Published: (2024)
by: Rao, Jackie, et al.
Published: (2024)
Similar Items
-
Building a stable classifier with the inflated argmax
by: Soloff, Jake A., et al.
Published: (2024) -
Assumption-free stability for ranking problems
by: Liang, Ruiting, et al.
Published: (2025) -
Can a calibration metric be both testable and actionable?
by: Rossellini, Raphael, et al.
Published: (2025) -
Bagging Provides Assumption-free Stability
by: Soloff, Jake A., et al.
Published: (2023) -
Personalizing black-box models for nonparametric regression with minimax optimality
by: Li, Sai, et al.
Published: (2026)