kernel function

Discovering Latent Causal Graphs from Spatiotemporal Data ICML 2025 From Fairness to Infinity: Outcome-Indistinguishable (Omni)Prediction in Evolving Graphs COLT 2025 Uncertainty Modelling in Under-Represented Languages with Bayesian Deep Gaussian Processes COLING 2025 Weighted Euclidean Distance Matrices over Mixed Continuous and Categorical Inputs for Gaussian Process Models AISTATS 2025 Contrastive Learning is Spectral Clustering on Similarity Graph ICLR 2024 Error bounds for any regression model using Gaussian processes with gradient information AISTATS 2024 Expanding Sparse Tuning for Low Memory Usage NeurIPS 2024 Linear Transformers with Learnable Kernel Functions are Better In-Context Models ACL 2024 On the Eigenvalue Decay Rates of a Class of Neural-Network Related Kernel Functions Defined on General Domains JMLR 2024 Sample-efficient Bayesian Optimisation Using Known Invariances NeurIPS 2024 Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee JMLR 2024 GPEX, A Framework For Interpreting Artificial Neural Networks NeurIPS 2023 Kernel QuantTree ICML 2023 Metrizing Weak Convergence with Maximum Mean Discrepancies JMLR 2023 Hyper-Convolution Networks for Biomedical Image Segmentation WACV 2022 Active Offline Policy Selection NeurIPS 2021 Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems AAAI 2021 Implicit Kernel Attention AAAI 2021 Learning GPLVM with arbitrary kernels using the unscented transformation AISTATS 2021 Sketch based Memory for Neural Networks AISTATS 2021 Graph Random Neural Features for Distance-Preserving Graph Representations ICML 2020 Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters NeurIPS 2020 A Representer Theorem for Deep Kernel Learning JMLR 2019 Bayesian Optimization Meets Riemannian Manifolds in Robot Learning CoRL 2019 Learnable Embedding Space for Efficient Neural Architecture Compression ICLR 2019