Variable Selection in Bayesian Smoothing Spline ANOVA Models: Application to Deterministic Computer Codes. - Brian J. Reich; Curtis B. Storlie; Howard D. Bondell (Technometrics, 2009)

Interquantile shrinkage and variable selection in quantile regression. - Liewen Jiang; Howard D. Bondell; Huixia Judy Wang (Comput. Stat. Data Anal., 2014)

Domain selection for the varying coefficient model via local polynomial regression. - Dehan Kong; Howard D. Bondell; Yichao Wu (Comput. Stat. Data Anal., 2015)

A Bayesian mixture model for clustering and selection of feature occurrence rates under mean constraints. - Qiwei Li 001; Michele Guindani; Brian J. Reich; Howard D. Bondell; Marina Vannucci (Stat. Anal. Data Min., 2017)

Outlier Detection and Robust Estimation in Nonparametric Regression. - Dehan Kong; Howard D. Bondell; Weining Shen (AISTATS, 2018)

Variational approximations using Fisher divergence. - Yue Yang; Ryan Martin; Howard D. Bondell (CoRR, 2019)

Best linear estimation via minimization of relative mean squared error. - Lin Su 001; Howard D. Bondell (Stat. Comput., 2019)

Bayesian variable selection for logistic regression. - Yiqing Tian; Howard D. Bondell; Alyson G. Wilson (Stat. Anal. Data Min., 2019)

Nonparametric Conditional Density Estimation In A Deep Learning Framework For Short-Term Forecasting. - David B. Huberman; Brian J. Reich; Howard D. Bondell (CoRR, 2020)

Solution paths for the generalized lasso with applications to spatially varying coefficients regression. - Yaqing Zhao; Howard D. Bondell (Comput. Stat. Data Anal., 2020)

Non-stationary Gaussian process discriminant analysis with variable selection for high-dimensional functional data. - Weichang Yu 001; Sara Wade; Howard D. Bondell; Lamiae Azizi (CoRR, 2021)

Federated Causal Discovery. - Erdun Gao; Junjia Chen; Li Shen 008; Tongliang Liu; Mingming Gong; Howard D. Bondell (CoRR, 2021)

Deep distribution regression. - Rui Li 061; Brian J. Reich; Howard D. Bondell (Comput. Stat. Data Anal., 2021)

Uncertainty Quantification in Depth Estimation via Constrained Ordinal Regression. - Dongting Hu; Liuhua Peng; Tingjin Chu; Xiaoxing Zhang; Yinian Mao; Howard D. Bondell; Mingming Gong (ECCV (2), 2022)

MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. - Erdun Gao; Ignavier Ng; Mingming Gong; Li Shen 008; Wei Huang; Tongliang Liu; Kun Zhang 001; Howard D. Bondell (NeurIPS, 2022)

Temporal and spectral governing dynamics of Australian hydrological streamflow time series. - Nick James; Howard D. Bondell (J. Comput. Sci., 2022)

Cold PAWS: Unsupervised class discovery and the cold-start problem. - Evelyn J. Mannix; Howard D. Bondell (CoRR, 2023)

Improved Prototypical Semi-Supervised Learning with Foundation Models: Prototype Selection, Parametric vMF-SNE Pretraining and Multi-view Pseudolabelling. - Evelyn J. Mannix; Howard D. Bondell (CoRR, 2023)

FedDAG: Federated DAG Structure Learning. - Erdun Gao; Junjia Chen; Li Shen 008; Tongliang Liu; Mingming Gong; Howard D. Bondell (Trans. Mach. Learn. Res., 2023)

Nonstationary Gaussian Process Discriminant Analysis With Variable Selection for High-Dimensional Functional Data. - Weichang Yu 001; Sara Wade; Howard D. Bondell; Lamiae Azizi (J. Comput. Graph. Stat., 2023)

Bayesian analysis of longitudinal data via empirical likelihood. - Jiangrong Ouyang; Howard D. Bondell (Comput. Stat. Data Anal., 2023)

Causal Discovery with Mixed Linear and Nonlinear Additive Noise Models: A Scalable Approach. - Wenqin Liu; Biwei Huang; Erdun Gao; Qiuhong Ke; Howard D. Bondell; Mingming Gong (CLeaR, 2024)

A Variational Framework for Estimating Continuous Treatment Effects with Measurement Error. - Erdun Gao; Howard D. Bondell; Wei Huang; Mingming Gong (ICLR, 2024)

Scalable and Robust Transformer Decoders for Interpretable Image Classification with Foundation Models. - Evelyn J. Mannix; Howard D. Bondell (CoRR, 2024)

Test-Time Augmentation Meets Variational Bayes. - Masanari Kimura; Howard D. Bondell (CoRR, 2024)

Faithful Label-free Knowledge Distillation. - Evelyn J. Mannix; Liam Hodgkinson; Howard D. Bondell (CoRR, 2024)

Density Ratio Estimation via Sampling along Generalized Geodesics on Statistical Manifolds. - Masanari Kimura; Howard D. Bondell (AISTATS, 2025)

Efficient sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm. - Alexander C. McLain; Anja Zgodic; Howard D. Bondell (Comput. Stat. Data Anal., 2025)

A Mixture of Exemplars Approach for Efficient Out-of-Distribution Detection with Foundation Models. - Evelyn J. Mannix; Howard D. Bondell (Trans. Mach. Learn. Res., 2025)

Theoretical and Practical Analysis of Fréchet Regression via Comparison Geometry. - Masanari Kimura; Howard D. Bondell (CoRR, 2025)

Generalized Power Priors for Improved Bayesian Inference with Historical Data. - Masanari Kimura; Howard D. Bondell (CoRR, 2025)