arXiv Machine Learning
Correcting Boundary Bias and Observation Independence in Bayesian Experimental Design
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Jun 9
Boundary Variance Inflation Causes Acquisition Bias in Gaussian Processes
arXiv:2606. 07561v1 Announce Type: new Abstract: Gaussian processes with stationary kernels on bounded domains exhibit inflated posterior variance near the boundary.
arXiv AI
Jul 7
JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference
arXiv:2512. 22999v2 Announce Type: replace-cross Abstract: We consider problems of parameter estimation where design variables can be actively optimized to maximize information gain.
arXiv Machine Learning
Aug 24
Actively Learning Joint Contours of Multiple Computer Experiments
The paper introduces a joint contour location (jCL) method for actively learning input configurations that simultaneously achieve specified responses across multiple computer experiments. By employing two distinct acquisition schemes—one for exploration and one for exploitation—along with a decision rule, the approach balances learning across multiple response surfaces and provides a natural stopping criterion when no solution exists. The method is demonstrated with Gaussian processes, multitask GPs, and deep GPs, outperforming existing single-response contour location strategies and optimization-based alternatives.
arXiv AI
Aug 26
Enhancing Bayesian Optimization and Active Learning Through Kernel Diversity
The paper introduces KENDO, a unified framework that combines Ensemble Gaussian Processes with disagreement‑aware acquisition strategies to address hyperparameter selection in Bayesian optimization and active learning. By replacing costly hyperparameter sampling with a kernel ensemble and adaptive Bayesian weighting, KENDO‑BO and KENDO‑AL provide self‑correcting mechanisms tailored to their respective tasks. Experiments on synthetic and real‑world benchmarks show that KENDO‑BO matches or outperforms state‑of‑the‑art methods while cutting computational cost up to fivefold, and KENDO‑AL delivers better predictive calibration with up to 27‑times speedup compared to MCMC‑based baselines.
arXiv AI
Jul 8
Rendering-Aware Bayesian 3D Gaussian Splatting with Native Uncertainty and Adaptive Complexity Control
arXiv:2607. 05522v1 Announce Type: cross Abstract: 3D Gaussian splatting (3DGS) is a strong representation for real-time novel-view synthesis, but its standard training pipeline relies on point estimates and hand-tuned heuristics, providing no native uncertainty or principled complexity control.
arXiv Machine Learning
Jun 30
Warm-Starting Iterative Gaussian Processes for Faster Sequential Inference
arXiv:2511. 16340v2 Announce Type: replace Abstract: Efficient Gaussian process (GP) inference is critical for sequential decision-making tasks such as active learning, online prediction, and Bayesian optimization.