neural tangent kernel

Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels ICML 2023 Bidirectional Learning for Offline Model-based Biological Sequence Design ICML 2023 Can Infinitely Wide Deep Nets Help Small-data Multi-label Learning? ACML 2023 Characterizing the spectrum of the NTK via a power series expansion ICLR 2023 Combinatorial Neural Bandits ICML 2023 Dataset Distillation with Convexified Implicit Gradients ICML 2023 Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks NeurIPS 2023 Feature selection and low test error in shallow low-rotation ReLU networks ICLR 2023 Few-shot Backdoor Attacks via Neural Tangent Kernels ICLR 2023 Fixing the NTK: From Neural Network Linearizations to Exact Convex Programs NeurIPS 2023 Gradient Descent in Neural Networks as Sequential Learning in Reproducing Kernel Banach Space ICML 2023 Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs ICLR 2023 Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension NeurIPS 2023 Neural Q-learning for solving PDEs JMLR 2023 Neural tangent kernel at initialization: linear width suffices UAI 2023 Neural (Tangent Kernel) Collapse NeurIPS 2023 Nonlinear Advantage: Trained Networks Might Not Be As Complex as You Think ICML 2023 NTK-approximating MLP Fusion for Efficient Language Model Fine-tuning ICML 2023 NTK-SAP: Improving neural network pruning by aligning training dynamics ICLR 2023 On the Asymptotic Learning Curves of Kernel Ridge Regression under Power-law Decay NeurIPS 2023 On the Convergence of Encoder-only Shallow Transformers NeurIPS 2023 On the Dynamics Under the Unhinged Loss and Beyond JMLR 2023 On the Neural Tangent Kernel Analysis of Randomly Pruned Neural Networks AISTATS 2023 Provable Guarantees for Neural Networks via Gradient Feature Learning NeurIPS 2023 Provably and Practically Efficient Neural Contextual Bandits ICML 2023