Howard D. Bondell

Orcid: 0000-0001-7743-0840

Affiliations:
  • University of Melbourne, VIC, Australia


According to our database1, Howard D. Bondell authored at least 26 papers between 2009 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Links

Online presence:

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Bibliography

2024
Test-Time Augmentation Meets Variational Bayes.
CoRR, 2024

Density Ratio Estimation via Sampling along Generalized Geodesics on Statistical Manifolds.
CoRR, 2024

Scalable and Robust Transformer Decoders for Interpretable Image Classification with Foundation Models.
CoRR, 2024

A Variational Framework for Estimating Continuous Treatment Effects with Measurement Error.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Causal Discovery with Mixed Linear and Nonlinear Additive Noise Models: A Scalable Approach.
Proceedings of the Causal Learning and Reasoning, 2024

2023
Bayesian analysis of longitudinal data via empirical likelihood.
Comput. Stat. Data Anal., November, 2023

Nonstationary Gaussian Process Discriminant Analysis With Variable Selection for High-Dimensional Functional Data.
J. Comput. Graph. Stat., April, 2023

FedDAG: Federated DAG Structure Learning.
Trans. Mach. Learn. Res., 2023

Improved Prototypical Semi-Supervised Learning with Foundation Models: Prototype Selection, Parametric vMF-SNE Pretraining and Multi-view Pseudolabelling.
CoRR, 2023

Cold PAWS: Unsupervised class discovery and the cold-start problem.
CoRR, 2023

2022
Temporal and spectral governing dynamics of Australian hydrological streamflow time series.
J. Comput. Sci., 2022

MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Uncertainty Quantification in Depth Estimation via Constrained Ordinal Regression.
Proceedings of the Computer Vision - ECCV 2022, 2022

2021
Deep distribution regression.
Comput. Stat. Data Anal., 2021

Federated Causal Discovery.
CoRR, 2021

Non-stationary Gaussian process discriminant analysis with variable selection for high-dimensional functional data.
CoRR, 2021

2020
Solution paths for the generalized lasso with applications to spatially varying coefficients regression.
Comput. Stat. Data Anal., 2020

Nonparametric Conditional Density Estimation In A Deep Learning Framework For Short-Term Forecasting.
CoRR, 2020

2019
Bayesian variable selection for logistic regression.
Stat. Anal. Data Min., 2019

Best linear estimation via minimization of relative mean squared error.
Stat. Comput., 2019

Variational approximations using Fisher divergence.
CoRR, 2019

2018
Outlier Detection and Robust Estimation in Nonparametric Regression.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
A Bayesian mixture model for clustering and selection of feature occurrence rates under mean constraints.
Stat. Anal. Data Min., 2017

2015
Domain selection for the varying coefficient model via local polynomial regression.
Comput. Stat. Data Anal., 2015

2014
Interquantile shrinkage and variable selection in quantile regression.
Comput. Stat. Data Anal., 2014

2009
Variable Selection in Bayesian Smoothing Spline ANOVA Models: Application to Deterministic Computer Codes.
Technometrics, 2009


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