{"total":40,"rows":[{"id":"2110.10804","title":"Identifiable Deep Generative Models via Sparse Decoding","created":"2021-10-20","cited_by_count":54,"area":null},{"id":"2410.15555","title":"Bayesian Concept Bottleneck Models with LLM Priors","created":"2024-10-21","cited_by_count":17,"area":null},{"id":"2211.05964","title":"Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits","created":"2022-11-11","cited_by_count":16,"area":null},{"id":"2104.12232","title":"Variational Inference in high-dimensional linear regression","created":"2021-04-25","cited_by_count":10,"area":null},{"id":"2205.10009","title":"The price of ignorance: how much does it cost to forget noise structure in low-rank matrix estimation?","created":"2022-05-20","cited_by_count":9,"area":null},{"id":"2305.14765","title":"Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference","created":"2023-05-24","cited_by_count":9,"area":null},{"id":"2111.04558","title":"Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes","created":"2021-11-08","cited_by_count":7,"area":null},{"id":"2206.00241","title":"Asymptotic Properties for Bayesian Neural Network in Besov Space","created":"2022-06-01","cited_by_count":7,"area":null},{"id":"2405.01304","title":"Misclassification bounds for PAC-Bayesian sparse deep learning","created":"2024-05-02","cited_by_count":7,"area":null},{"id":"2107.06936","title":"Performance of Bayesian linear regression in a model with mismatch","created":"2021-07-14","cited_by_count":6,"area":null},{"id":"2409.01687","title":"A sparse PAC-Bayesian approach for high-dimensional quantile prediction","created":"2024-09-03","cited_by_count":6,"area":null},{"id":"2204.01668","title":"Scalable Spike-and-Slab","created":"2022-04-04","cited_by_count":5,"area":null},{"id":"2208.05447","title":"Robust Methods for High-Dimensional Linear Learning","created":"2022-08-10","cited_by_count":5,"area":null},{"id":"2406.00778","title":"Bayesian Joint Additive Factor Models for Multiview Learning","created":"2024-06-02","cited_by_count":5,"area":null},{"id":"2307.07172","title":"FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout","created":"2023-07-14","cited_by_count":4,"area":null},{"id":"2505.08288","title":"High-dimensional Bayesian Tobit regression for censored response with Horseshoe prior","created":"2025-05-13","cited_by_count":4,"area":null},{"id":"2102.00305","title":"Spike and slab Bayesian sparse principal component analysis","created":"2021-01-30","cited_by_count":3,"area":null},{"id":"2108.06446","title":"A fast asynchronous MCMC sampler for sparse Bayesian inference","created":"2021-08-14","cited_by_count":3,"area":null},{"id":"2302.00755","title":"Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery","created":"2023-02-01","cited_by_count":3,"area":null},{"id":"2102.12261","title":"Sparse online variational Bayesian regression","created":"2021-02-24","cited_by_count":2,"area":null},{"id":"2209.08139","title":"Sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm","created":"2022-09-16","cited_by_count":2,"area":null},{"id":"2210.12957","title":"On the optimization and pruning for Bayesian deep learning","created":"2022-10-24","cited_by_count":2,"area":null},{"id":"2404.03764","title":"Covariate-Elaborated Robust Partial Information Transfer with Conditional Spike-and-Slab Prior","created":"2024-03-30","cited_by_count":2,"area":null},{"id":"2505.22211","title":"Handling bounded response in high dimensions: a Horseshoe prior Bayesian Beta regression approach","created":"2025-05-28","cited_by_count":2,"area":null},{"id":"2206.00853","title":"Masked Bayesian Neural Networks : Computation and Optimality","created":"2022-06-02","cited_by_count":1,"area":null},{"id":"2405.19016","title":"Adaptive posterior concentration rates for sparse high-dimensional linear regression with random design and unknown error variance","created":"2024-05-29","cited_by_count":1,"area":null},{"id":"2412.20824","title":"Isoperimetry is All We Need: Langevin Posterior Sampling for RL with Sublinear Regret","created":"2024-12-30","cited_by_count":1,"area":null},{"id":"2502.14281","title":"Correcting Noisy Multilabel Predictions: Modeling Label Noise through Latent Space Shifts","created":"2025-02-20","cited_by_count":1,"area":null},{"id":"2601.20528","title":"Spectral Bayesian Regression on the Sphere","created":"2026-01-28","cited_by_count":1,"area":null},{"id":"2603.20929","title":"Stability of Sequential and Parallel Coordinate Ascent Variational Inference","created":"2026-03-21","cited_by_count":1,"area":null},{"id":"2305.15754","title":"Bayesian Analysis for Over-parameterized Linear Model via Effective Spectra","created":"2023-05-25","cited_by_count":0,"area":null},{"id":"2307.05558","title":"From Estimation to Sampling for Bayesian Linear Regression with Spike-and-Slab Prior","created":"2023-07-09","cited_by_count":0,"area":null},{"id":"2311.06395","title":"A statistical perspective on algorithm unrolling models for inverse problems","created":"2023-11-10","cited_by_count":0,"area":null},{"id":"2410.15381","title":"High-dimensional prediction for count response via sparse exponential weights","created":"2024-10-20","cited_by_count":0,"area":null},{"id":"2412.17354","title":"Bayesian penalized empirical likelihood and Markov Chain Monte Carlo sampling","created":"2024-12-23","cited_by_count":0,"area":null},{"id":"2501.03130","title":"SpinSVAR: Estimating Structural Vector Autoregression Assuming Sparse Input","created":"2025-01-06","cited_by_count":0,"area":null},{"id":"2503.04386","title":"Time-varying Factor Augmented Vector Autoregression with Grouped Sparse Autoencoder","created":"2025-03-06","cited_by_count":0,"area":null},{"id":"2605.29908","title":"Joint Model and Data Sparsification via the Marginal Likelihood","created":"2026-05-28","cited_by_count":0,"area":null},{"id":"2606.27090","title":"Beyond Global Divergences: A Local-Mass Perspective on Bayesian Inference","created":"2026-06-25","cited_by_count":0,"area":null},{"id":"2609.33550","title":"Sparsity by Default: The Theory and Practice of ARD in Gaussian Process Regression for Variable Selection","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}