:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Lee, Changwoo J., Zito, Alessandro, Sang, Huiyan, Dunson, David B.
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Methodology Machine Learning
Accesso online:	https://arxiv.org/abs/2402.07048
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Marginally interpretable spatial logistic regression with bridge processes
di: Lee, Changwoo J., et al.
Pubblicazione: (2024)

Learning discrete Bayesian networks with hierarchical Dirichlet shrinkage
di: Dombowsky, Alexander, et al.
Pubblicazione: (2025)

Pareto optimal proxy metrics
di: Zito, Alessandro, et al.
Pubblicazione: (2023)

Bayesian nonparametric modeling of latent partitions via Stirling-gamma priors
di: Zito, Alessandro, et al.
Pubblicazione: (2023)

Inference on covariance structure in high-dimensional multi-view data
di: Mauri, Lorenzo, et al.
Pubblicazione: (2025)

Sequential Gibbs Posteriors with Applications to Principal Component Analysis
di: Winter, Steven, et al.
Pubblicazione: (2023)

Spectral decomposition-assisted multi-study factor analysis
di: Mauri, Lorenzo, et al.
Pubblicazione: (2025)

Nearest Neighbor Dirichlet Mixtures
di: Chattopadhyay, Shounak, et al.
Pubblicazione: (2020)

Nonparametric IPSS: Fast, flexible feature selection with false discovery control
di: Melikechi, Omar, et al.
Pubblicazione: (2024)

Scalable and robust regression models for continuous proportional data
di: Lee, Changwoo J., et al.
Pubblicazione: (2025)

SBAMDT: Bayesian Additive Decision Trees with Adaptive Soft Semi-multivariate Split Rules
di: Lamprinakou, Stamatina, et al.
Pubblicazione: (2025)

A General Causal Inference Framework for Cross-Sectional Observational Data
di: Zhao, Yonghe, et al.
Pubblicazione: (2024)

Logistic Variational Bayes Revisited
di: Komodromos, Michael, et al.
Pubblicazione: (2024)

Design-marginal calibration of Gaussian process predictive distributions: Bayesian and conformal approaches
di: Pion, Aurélien, et al.
Pubblicazione: (2025)

Marginally Interpretable Spatial Logistic Regression With Bridge Processes
di: Changwoo J. Lee, et al.
Pubblicazione: (2026)

F-measure Maximizing Logistic Regression
di: Okabe, Masaaki, et al.
Pubblicazione: (2019)

Bayesian Joint Additive Factor Models for Multiview Learning
di: Anceschi, Niccolo, et al.
Pubblicazione: (2024)

A note on simulation methods for the Dirichlet-Laplace prior
di: Gruber, Luis, et al.
Pubblicazione: (2025)

Understanding overfitting in random forest for probability estimation: a visualization and simulation study
di: Barreñada, Lasai, et al.
Pubblicazione: (2024)

Data-Driven Logistic Regression Ensembles With Applications in Genomics
di: Christidis, Anthony-Alexander, et al.
Pubblicazione: (2021)

De-confounding Representation Learning for Counterfactual Inference on Continuous Treatment via Generative Adversarial Network
di: Zhao, Yonghe, et al.
Pubblicazione: (2023)

ReLU integral probability metric and its applications
di: Park, Yuha, et al.
Pubblicazione: (2025)

On the Wasserstein Geodesic Principal Component Analysis of probability measures
di: Vesseron, Nina, et al.
Pubblicazione: (2025)

Optimal differentially private kernel learning with random projection
di: Lee, Bonwoo, et al.
Pubblicazione: (2025)

Inference at the data's edge: Gaussian processes for modeling and inference under model-dependency, poor overlap, and extrapolation
di: Cho, Soonhong, et al.
Pubblicazione: (2024)

Scalable Inference for Bayesian Multinomial Logistic-Normal Dynamic Linear Models
di: Saxena, Manan, et al.
Pubblicazione: (2024)

Logistic regression models for patient-level prediction based on massive observational data: Do we need all data?
di: John, Luis H., et al.
Pubblicazione: (2020)

Multi-marginal Schrödinger Bridges with Iterative Reference Refinement
di: Shen, Yunyi, et al.
Pubblicazione: (2024)

Poisson process factorization for mutational signature analysis with genomic covariates
di: Zito, Alessandro, et al.
Pubblicazione: (2025)

Bayesian modeling of nearly mutually orthogonal processes
di: Matuk, James, et al.
Pubblicazione: (2022)

A Flexible Empirical Bayes Approach to Generalized Linear Models, with Applications to Sparse Logistic Regression
di: Xie, Dongyue, et al.
Pubblicazione: (2026)

Modelling non-stationary extremal dependence through a geometric approach
di: Murphy-Barltrop, C. J. R., et al.
Pubblicazione: (2025)

Machine learning to optimize precision in the analysis of randomized trials: A journey in pre-specified, yet data-adaptive learning
di: Balzer, Laura B., et al.
Pubblicazione: (2025)

Online scalable Gaussian processes with conformal prediction for guaranteed coverage
di: Xu, Jinwen, et al.
Pubblicazione: (2024)

Spectral clustering for dependent community Hawkes process models of temporal networks
di: Zhao, Lingfei, et al.
Pubblicazione: (2025)

Nonparametric estimation of conditional probability distributions using a generative approach based on conditional push-forward neural networks
di: Franco, Nicola Rares, et al.
Pubblicazione: (2025)

Non-parametric efficient estimation of marginal structural models with multi-valued time-varying treatments
di: Martin, Axel, et al.
Pubblicazione: (2024)

Deep-testing: the case of dependence detection
di: Geenens, Gery, et al.
Pubblicazione: (2026)

Ensemble Prediction via Covariate-dependent Stacking
di: Wakayama, Tomoya, et al.
Pubblicazione: (2024)

Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data
di: Karlsson, Rickard, et al.
Pubblicazione: (2025)