5 papers
Posterior Contraction for Sparse Neural Networks in Besov Spaces with Intrinsic Dimensionality
Kyeongwon Lee, Lizhen Lin, Jaewoo Park +1
This work establishes that sparse Bayesian neural networks achieve optimal posterior contraction rates over anisotropic Besov spaces and their hierarchical compositions. These stru…
-norm posterior contraction in Gaussian models with unknown variance
Seonghyun Jeong
The testing-based approach is a fundamental tool for establishing posterior contraction rates. Although the Hellinger metric is attractive owing to the existence of a desirable tes…
Penalty-Induced Basis Exploration for Bayesian Splines
Sunwoo Lim, Sihyeon Pyeon, Seonghyun Jeong
Spline basis exploration via Bayesian model selection is a widely employed strategy for determining the optimal set of basis terms in nonparametric regression. However, despite its…
ADOPT: Modified Adam Can Converge with Any with the Optimal Rate
Shohei Taniguchi, Keno Harada, Gouki Minegishi +7
Adam is one of the most popular optimization algorithms in deep learning. However, it is known that Adam does not converge in theory unless choosing a hyperparameter, i.e., ,…
Unsupervised Outlier Detection using Random Subspace and Subsampling Ensembles of Dirichlet Process Mixtures
Dongwook Kim, Juyeon Park, Hee Cheol Chung +1
Probabilistic mixture models are recognized as effective tools for unsupervised outlier detection owing to their interpretability and global characteristics. Among these, Dirichlet…