kernel methods

Physics-Informed Bayesian Optimization of Variational Quantum Circuits NeurIPS 2023 Practical Markov Boundary Learning without Strong Assumptions AAAI 2023 Reproducing Kernels and New Approaches in Compositional Data Analysis JMLR 2023 Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge ICML 2023 Revisiting minimum description length complexity in overparameterized models JMLR 2023 Reward Learning as Doubly Nonparametric Bandits: Optimal Design and Scaling Laws AISTATS 2023 Robust Concept Erasure via Kernelized Rate-Distortion Maximization NeurIPS 2023 Sample based Explanations via Generalized Representers NeurIPS 2023 Sample Complexity of Kernel-Based Q-Learning AISTATS 2023 Scalable Optimal Margin Distribution Machine IJCAI 2023 Self-supervised learning with rotation-invariant kernels ICLR 2023 Sequential Predictive Two-Sample and Independence Testing NeurIPS 2023 Sparse Spectral Bayesian Permanental Process with Generalized Kernel AISTATS 2023 Statistical Robustness of Empirical Risks in Machine Learning JMLR 2023 Subquadratic Algorithms for Kernel Matrices via Kernel Density Estimation ICLR 2023 Symphony in the Latent Space: Provably Integrating High-Dimensional Techniques with Non-linear Machine Learning Models AAAI 2023 Tanimoto Random Features for Scalable Molecular Machine Learning NeurIPS 2023 The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time JMLR 2023 Time Series Kernels based on Nonlinear Vector AutoRegressive Delay Embeddings NeurIPS 2023 Toward Large Kernel Models ICML 2023 Towards a Persistence Diagram that is Robust to Noise and Varied Densities ICML 2023 Towards a Unified Analysis of Kernel-based Methods Under Covariate Shift NeurIPS 2023 Two Sample Testing in High Dimension via Maximum Mean Discrepancy JMLR 2023 Two-stage Kernel Bayesian Optimization in High Dimensions UAI 2023 Understanding Backdoor Attacks through the Adaptability Hypothesis ICML 2023