Faithful Label-free Knowledge Distillation.
CoRR, 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
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
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
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
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
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
Outlier Detection and Robust Estimation in Nonparametric Regression.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018
A Bayesian mixture model for clustering and selection of feature occurrence rates under mean constraints.
Stat. Anal. Data Min., 2017
Domain selection for the varying coefficient model via local polynomial regression.
Comput. Stat. Data Anal., 2015
Interquantile shrinkage and variable selection in quantile regression.
Comput. Stat. Data Anal., 2014
Variable Selection in Bayesian Smoothing Spline ANOVA Models: Application to Deterministic Computer Codes.
Technometrics, 2009