gaussian process

Supervising the Multi-Fidelity Race of Hyperparameter Configurations NeurIPS 2022 Symplectic Spectrum Gaussian Processes: Learning Hamiltonians from Noisy and Sparse Data NeurIPS 2022 Temporal Knowledge Graph Completion with Approximated Gaussian Process Embedding COLING 2022 The Fast Kernel Transform AISTATS 2022 Towards Learning Universal Hyperparameter Optimizers with Transformers NeurIPS 2022 Transferability Estimation Using Bhattacharyya Class Separability CVPR 2022 Transfer Learning with Gaussian Processes for Bayesian Optimization AISTATS 2022 Transformers Can Do Bayesian Inference ICLR 2022 Traversing Time with Multi-Resolution Gaussian Process State-Space Models L4DC 2022 Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces NeurIPS 2022 Uncertainty Calibration for Deep Audio Classifiers Interspeech 2022 Uncertainty Quantification for Bayesian Optimization AISTATS 2022 Variational Gaussian Processes: A Functional Analysis View AISTATS 2022 Variational multiple shooting for Bayesian ODEs with Gaussian processes UAI 2022 Variational nearest neighbor Gaussian process ICML 2022 Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes ICML 2022 Weighted Gaussian Process Bandits for Non-stationary Environments AISTATS 2022 Activation-level uncertainty in deep neural networks ICLR 2021 Active Assessment of Prediction Services as Accuracy Surface Over Attribute Combinations NeurIPS 2021 Active Learning with Maximum Margin Sparse Gaussian Processes AISTATS 2021 A Distribution-dependent Analysis of Meta Learning ICML 2021 A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance NeurIPS 2021 Adversarial Robustness Guarantees for Random Deep Neural Networks ICML 2021 A Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning NeurIPS 2021 A general linear-time inference method for Gaussian Processes on one dimension JMLR 2021