kernel methods

Time Series Analysis using a Kernel based Multi-Modal Uncertainty Decomposition Framework UAI 2020 Towards Understanding Hierarchical Learning: Benefits of Neural Representations NeurIPS 2020 Two-sample Testing Using Deep Learning AISTATS 2020 Unbalanced Sobolev Descent NeurIPS 2020 Variational Adversarial Kernel Learned Imitation Learning AAAI 2020 Walsh-Hadamard Variational Inference for Bayesian Deep Learning NeurIPS 2020 When Do Neural Networks Outperform Kernel Methods? NeurIPS 2020 Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? --- A Neural Tangent Kernel Perspective NeurIPS 2020 Wide Neural Networks with Bottlenecks are Deep Gaussian Processes JMLR 2020 Winnowing with Gradient Descent COLT 2020 ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks ICCV 2019 A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations CVPR 2019 A Kernel Multiple Change-point Algorithm via Model Selection JMLR 2019 A Kernel Perspective for Regularizing Deep Neural Networks ICML 2019 A Kernel Random Matrix-Based Approach for Sparse PCA ICLR 2019 A Kernel Theory of Modern Data Augmentation ICML 2019 A maximum-mean-discrepancy goodness-of-fit test for censored data AISTATS 2019 Argumentative Evidences Classification and Argument Scheme Detection Using Tree Kernels ACL 2019 A Stein–Papangelou Goodness-of-Fit Test for Point Processes AISTATS 2019 Autoencoding any Data through Kernel Autoencoders AISTATS 2019 Band-Limited Gaussian Processes: The Sinc Kernel NeurIPS 2019 Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance COLT 2019 Comparing distributions: ℓ_1 geometry improves kernel two-sample testing NeurIPS 2019 Convex Formulations for Fair Principal Component Analysis AAAI 2019 Copula Multi-label Learning NeurIPS 2019