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stat.ML2026
Efficient Deep Gaussian Process Surrogates via Mini-Batch Latent MAP Estimation
Chun-Yi Chang, Chih-Li Sung
Deep Gaussian processes (DGPs) are flexible surrogate models for computer experiments with nonstationary response surfaces, but inference over their latent representations remains…
stat.ML2025
Deep Intrinsic Coregionalization Multi-Output Gaussian Process Surrogate with Active Learning
Chun-Yi Chang, Chih-Li Sung
Deep Gaussian Processes (DGPs) are powerful surrogate models known for their flexibility and ability to capture complex functions. However, extending them to multi-output settings…
stat.ML2024★ 1 cited
Uncertainty-Aware Out-of-Distribution Detection with Gaussian Processes
Yang Chen, Chih-Li Sung, Arpan Kusari +2
Deep neural networks (DNNs) are often constructed under the closed-world assumption, which may fail to generalize to the out-of-distribution (OOD) data. This leads to DNNs producin…