kernel methods

Conditional Contrastive Learning with Kernel ICLR 2022 Consistent Interpolating Ensembles via the Manifold-Hilbert Kernel NeurIPS 2022 cosFormer: Rethinking Softmax In Attention ICLR 2022 Differentially Private Learning with Margin Guarantees NeurIPS 2022 Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression JMLR 2022 Doubly Mixed-Effects Gaussian Process Regression AISTATS 2022 Efficient Non-local Contrastive Attention for Image Super-resolution AAAI 2022 Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces NeurIPS 2022 Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination NeurIPS 2022 Fast Graph Neural Tangent Kernel via Kronecker Sketching AAAI 2022 Fast Instrument Learning with Faster Rates NeurIPS 2022 Fast Neural Kernel Embeddings for General Activations NeurIPS 2022 Feature Kernel Distillation ICLR 2022 Feature learning and random features in standard finite-width convolutional neural networks: An empirical study UAI 2022 Feature screening with kernel knockoffs AISTATS 2022 Few-shot Learning for Feature Selection with Hilbert-Schmidt Independence Criterion NeurIPS 2022 Forward Operator Estimation in Generative Models with Kernel Transfer Operators ICML 2022 Functional Output Regression with Infimal Convolution: Exploring the Huber and $ε$-insensitive Losses ICML 2022 Fusion Multiple Kernel K-means AAAI 2022 Gaussian Process Parameter Estimation Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits JMLR 2022 Gaussian process regression: Optimality, robustness, and relationship with kernel ridge regression JMLR 2022 Generalization Through the Lens of Leave-One-Out Error ICLR 2022 Generic Coreset for Scalable Learning of Monotonic Kernels: Logistic Regression, Sigmoid and more ICML 2022 Giga-scale Kernel Matrix-Vector Multiplication on GPU NeurIPS 2022 Graph Neural Network Bandits NeurIPS 2022