gaussian process

Multi-armed bandits for resource efficient, online optimization of language model pre-training: the use case of dynamic masking ACL 2023 Multi-Fidelity Bayesian Optimization with Unreliable Information Sources AISTATS 2023 Multilateral Semantic Relations Modeling for Image Text Retrieval CVPR 2023 Multilevel Bayesian Quadrature AISTATS 2023 Multi-view Collaborative Gaussian Process Dynamical Systems JMLR 2023 Neural Diffusion Processes ICML 2023 Neural signature kernels as infinite-width-depth-limits of controlled ResNets ICML 2023 Nonparametric Gaussian Process Covariances via Multidimensional Convolutions AISTATS 2023 No-regret Sample-efficient Bayesian Optimization for Finding Nash Equilibria with Unknown Utilities AISTATS 2023 On the Calibration and Uncertainty with Pólya-Gamma Augmentation for Dialog Retrieval Models AAAI 2023 On the role of model uncertainties in Bayesian optimisation UAI 2023 On the Sublinear Regret of GP-UCB NeurIPS 2023 Optimistic Active Exploration of Dynamical Systems NeurIPS 2023 Optimizing Hyperparameters with Conformal Quantile Regression ICML 2023 PFNs4BO: In-Context Learning for Bayesian Optimization ICML 2023 Physics-enhanced Gaussian Process Variational Autoencoder L4DC 2023 Physics-Guided Active Learning of Environmental Flow Fields L4DC 2023 Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior NeurIPS 2023 Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds NeurIPS 2023 POV-SLAM: Probabilistic Object-Aware Variational SLAM in Semi-Static Environments RSS 2023 Probabilistic Invariance for Gaussian Process State Space Models L4DC 2023 Probabilistic Safeguard for Reinforcement Learning Using Safety Index Guided Gaussian Process Models L4DC 2023 Quantum Bayesian Optimization NeurIPS 2023 Quasi-Bayesian nonparametric density estimation via autoregressive predictive updates UAI 2023 Random Grid Neural Processes for Parametric Partial Differential Equations ICML 2023