:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Osborne, Nathan, Peterson, Christine B., Vannucci, Marina
Format:	Preprint
Published:	2020
Subjects:	Methodology Machine Learning
Online Access:	https://arxiv.org/abs/2010.13229
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Bayesian Varying-Effects Vector Autoregressive Models for Inference of Brain Connectivity Networks and Covariate Effects in Pediatric Traumatic Brain Injury
by: Ren, Yangfan, et al.
Published: (2024)

Bayesian network-guided sparse regression with flexible varying effects
by: Ren, Yangfan, et al.
Published: (2024)

Bayesian Controlled FDR Variable Selection via Parameter-Expanded Latent Knockoffs
by: Focardi-Olmi, Lorenzo, et al.
Published: (2024)

Causal Effect Estimation using identifiable Variational AutoEncoder with Latent Confounders and Post-Treatment Variables
by: Xie, Yang, et al.
Published: (2024)

Tree-Based Predictive Models for Noisy Input Data
by: McCoy, Kevin, et al.
Published: (2026)

Weighted Sum-of-Trees Model for Clustered Data
by: McCoy, Kevin, et al.
Published: (2026)

Identifiable Deep Latent Variable Models for MNAR Data
by: Xie, Huiming, et al.
Published: (2026)

Robust Variable Selection and Estimation Via Adaptive Elastic Net S-Estimators for Linear Regression
by: Kepplinger, David
Published: (2021)

Latent Variable Estimation in Bayesian Black-Litterman Models
by: Lin, Thomas Y. L., et al.
Published: (2025)

Bayesian temporal biclustering with applications to multi-subject neuroscience studies
by: Ricci, Federica Zoe, et al.
Published: (2024)

Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data
by: Oprescu, Miruna, et al.
Published: (2024)

pared: Model selection using multi-objective optimization
by: Das, Priyam, et al.
Published: (2025)

Stability Selection via Variable Decorrelation
by: Nouraie, Mahdi, et al.
Published: (2025)

Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables
by: Xie, Feng, et al.
Published: (2023)

Variable Selection for Comparing High-dimensional Time-Series Data
by: Mitsuzawa, Kensuke, et al.
Published: (2024)

Tuning-Free Maximum Likelihood Training of Latent Variable Models via Coin Betting
by: Sharrock, Louis, et al.
Published: (2023)

Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams
by: Cho, Brian, et al.
Published: (2024)

Logistic Variational Bayes Revisited
by: Komodromos, Michael, et al.
Published: (2024)

A Latent Causal Inference Framework for Ordinal Variables
by: Scauda, Martina, et al.
Published: (2025)

Chiseling: Powerful and Valid Subgroup Selection via Interactive Machine Learning
by: Cheng, Nathan, et al.
Published: (2025)

Efficient Online Variational Estimation via Monte Carlo Sampling
by: Chagneux, Mathis, et al.
Published: (2026)

Penalized Generative Variable Selection
by: Wang, Tong, et al.
Published: (2024)

Robust Variable Selection for High-dimensional Regression with Missing Data and Measurement Errors
by: Zhang, Zhenhao, et al.
Published: (2024)

Causal Inference with Latent Variables: Recent Advances and Future Prospectives
by: Zhu, Yaochen, et al.
Published: (2024)

Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Data
by: Luo, Gongxu, et al.
Published: (2025)

A Hierarchical Variational Graph Fused Lasso for Recovering Relative Rates in Spatial Compositional Data
by: Teixeira, Joaquim Valerio, et al.
Published: (2025)

Linear Models, Variable Selection, Artificial Intelligence
by: Alrawkan, By Riyadh, et al.
Published: (2026)

Automating the Selection of Proxy Variables of Unmeasured Confounders
by: Xie, Feng, et al.
Published: (2024)

Diffusion-Driven High-Dimensional Variable Selection
by: Wang, Minjie, et al.
Published: (2025)

Variable Selection Methods for Multivariate, Functional, and Complex Biomedical Data in the AI Age
by: Matabuena, Marcos
Published: (2025)

Anchored Variational Inference for Personalized Sequential Latent-State Models
by: Guo, Xingche
Published: (2026)

Agglomerative Hierarchical Clustering for Selecting Valid Instrumental Variables
by: Apfel, Nicolas, et al.
Published: (2021)

Efficient Synthetic Network Generation via Latent Embedding Reconstruction
by: Jiang, Feifan, et al.
Published: (2026)

Virtual Dummies: Enabling Scalable FDR-Controlled Variable Selection via Sequential Sampling of Null Features
by: Koka, Taulant, et al.
Published: (2026)

Semiparametric Latent ANOVA Model for Event-Related Potentials
by: Yu, Cheng-Han, et al.
Published: (2023)

Hard and Soft EM in Bayesian Network Learning from Incomplete Data
by: Ruggieri, Andrea, et al.
Published: (2020)

A Graphical Approach to State Variable Selection in Off-policy Learning
by: Andersen, Joakim Blach, et al.
Published: (2025)

Asymptotic Inference for Multi-Stage Stationary Treatment Policy with Variable Selection
by: Gao, Daiqi, et al.
Published: (2023)

Estimating the Number of Components in Finite Mixture Models via Variational Approximation
by: Wang, Chenyang, et al.
Published: (2024)

Bias-Reduced Estimation of Finite Mixtures: An Application to Latent Group Structures in Panel Data
by: Langevin, Raphaël
Published: (2026)