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ORCIDHoward D. Bondell
Person information
- affiliation: University of Melbourne, VIC, Australia
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2020 – today
- 2024
[c5]Wenqin Liu, Biwei Huang, Erdun Gao, Qiuhong Ke, Howard D. Bondell, Mingming Gong:
Causal Discovery with Mixed Linear and Nonlinear Additive Noise Models: A Scalable Approach. CLeaR 2024: 1237-1263- [c4]Erdun Gao, Howard D. Bondell, Wei Huang, Mingming Gong:
A Variational Framework for Estimating Continuous Treatment Effects with Measurement Error. ICLR 2024 - [i12]Evelyn J. Mannix, Howard D. Bondell:
Scalable and Robust Transformer Decoders for Interpretable Image Classification with Foundation Models. CoRR abs/2403.04125 (2024) - [i11]Masanari Kimura, Howard D. Bondell:
Density Ratio Estimation via Sampling along Generalized Geodesics on Statistical Manifolds. CoRR abs/2406.18806 (2024) - [i10]Masanari Kimura, Howard D. Bondell:
Test-Time Augmentation Meets Variational Bayes. CoRR abs/2409.12587 (2024) - [i9]Evelyn J. Mannix, Liam Hodgkinson, Howard D. Bondell:
Faithful Label-free Knowledge Distillation. CoRR abs/2411.15239 (2024) - 2023
- [j12]Jiangrong Ouyang, Howard D. Bondell
:
Bayesian analysis of longitudinal data via empirical likelihood. Comput. Stat. Data Anal. 187: 107785 (2023) - [j11]Weichang Yu, Sara Wade, Howard D. Bondell, Lamiae Azizi:
Nonstationary Gaussian Process Discriminant Analysis With Variable Selection for High-Dimensional Functional Data. J. Comput. Graph. Stat. 32(2): 588-600 (2023) - [j10]Erdun Gao, Junjia Chen, Li Shen, Tongliang Liu, Mingming Gong, Howard D. Bondell:
FedDAG: Federated DAG Structure Learning. Trans. Mach. Learn. Res. 2023 (2023) - [i8]Evelyn J. Mannix, Howard D. Bondell:
Cold PAWS: Unsupervised class discovery and the cold-start problem. CoRR abs/2305.10071 (2023) - [i7]Evelyn J. Mannix, Howard D. Bondell:
Improved Prototypical Semi-Supervised Learning with Foundation Models: Prototype Selection, Parametric vMF-SNE Pretraining and Multi-view Pseudolabelling. CoRR abs/2311.17093 (2023) - 2022
- [j9]Nick James, Howard D. Bondell:
Temporal and spectral governing dynamics of Australian hydrological streamflow time series. J. Comput. Sci. 63: 101767 (2022) - [c3]Dongting Hu, Liuhua Peng, Tingjin Chu, Xiaoxing Zhang, Yinian Mao, Howard D. Bondell, Mingming Gong:
Uncertainty Quantification in Depth Estimation via Constrained Ordinal Regression. ECCV (2) 2022: 237-256 - [c2]Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, Howard D. Bondell:
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. NeurIPS 2022 - [i6]Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, Howard D. Bondell:
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. CoRR abs/2205.13869 (2022) - 2021
- [j8]Rui Li, Brian J. Reich, Howard D. Bondell:
Deep distribution regression. Comput. Stat. Data Anal. 159: 107203 (2021) - [i5]Weichang Yu, Sara Wade, Howard D. Bondell, Lamiae Azizi:
Non-stationary Gaussian process discriminant analysis with variable selection for high-dimensional functional data. CoRR abs/2109.14171 (2021) - [i4]Erdun Gao, Junjia Chen, Li Shen, Tongliang Liu, Mingming Gong, Howard D. Bondell:
Federated Causal Discovery. CoRR abs/2112.03555 (2021) - 2020
- [j7]Yaqing Zhao, Howard D. Bondell:
Solution paths for the generalized lasso with applications to spatially varying coefficients regression. Comput. Stat. Data Anal. 142 (2020) - [i3]David B. Huberman, Brian J. Reich, Howard D. Bondell:
Nonparametric Conditional Density Estimation In A Deep Learning Framework For Short-Term Forecasting. CoRR abs/2008.07653 (2020)
2010 – 2019
- 2019
- [j6]Lin Su, Howard D. Bondell:
Best linear estimation via minimization of relative mean squared error. Stat. Comput. 29(1): 33-42 (2019) - [j5]Yiqing Tian, Howard D. Bondell, Alyson G. Wilson:
Bayesian variable selection for logistic regression. Stat. Anal. Data Min. 12(5): 378-393 (2019) - [i2]Rui Li, Howard D. Bondell, Brian J. Reich:
Deep Distribution Regression. CoRR abs/1903.06023 (2019) - [i1]Yue Yang, Ryan Martin, Howard D. Bondell:
Variational approximations using Fisher divergence. CoRR abs/1905.05284 (2019) - 2018
- [c1]Dehan Kong, Howard D. Bondell, Weining Shen:
Outlier Detection and Robust Estimation in Nonparametric Regression. AISTATS 2018: 208-216 - 2017
- [j4]Qiwei Li, Michele Guindani, Brian J. Reich, Howard D. Bondell, Marina Vannucci:
A Bayesian mixture model for clustering and selection of feature occurrence rates under mean constraints. Stat. Anal. Data Min. 10(6): 393-409 (2017) - 2015
- [j3]Dehan Kong, Howard D. Bondell, Yichao Wu:
Domain selection for the varying coefficient model via local polynomial regression. Comput. Stat. Data Anal. 83: 236-250 (2015) - 2014
- [j2]Liewen Jiang, Howard D. Bondell, Huixia Judy Wang:
Interquantile shrinkage and variable selection in quantile regression. Comput. Stat. Data Anal. 69: 208-219 (2014)
2000 – 2009
- 2009
- [j1]Brian J. Reich, Curtis B. Storlie, Howard D. Bondell:
Variable Selection in Bayesian Smoothing Spline ANOVA Models: Application to Deterministic Computer Codes. Technometrics 51(2): 110-120 (2009)
Coauthor Index
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