graph laplacian

LDLE: Low Distortion Local Eigenmaps JMLR 2021 Learnable Motion Coherence for Correspondence Pruning CVPR 2021 Post-processing for Individual Fairness NeurIPS 2021 Rethinking Graph Regularization for Graph Neural Networks AAAI 2021 Strong Consistency, Graph Laplacians, and the Stochastic Block Model JMLR 2021 The decomposition of the higher-order homology embedding constructed from the k-Laplacian NeurIPS 2021 Connecting Spectral Clustering to Maximum Margins and Level Sets JMLR 2020 Consistency of Semi-Supervised Learning Algorithms on Graphs: Probit and One-Hot Methods JMLR 2020 Embedding Conjugate Gradient in Learning Random Walks for Landscape Connectivity Modeling in Conservation IJCAI 2020 Exploration in Reinforcement Learning with Deep Covering Options ICLR 2020 Laplacian-Regularized Graph Bandits: Algorithms and Theoretical Analysis AISTATS 2020 Option Discovery in the Absence of Rewards with Manifold Analysis ICML 2020 Skill Rating for Multiplayer Games. Introducing Hypernode Graphs and their Spectral Theory JMLR 2020 A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening NeurIPS 2019 Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces NeurIPS 2019 Improved Semi-Supervised Learning with Multiple Graphs AISTATS 2019 LanczosNet: Multi-Scale Deep Graph Convolutional Networks ICLR 2019 Learning by Unsupervised Nonlinear Diffusion JMLR 2019 Structured Graph Learning Via Laplacian Spectral Constraints NeurIPS 2019 The Laplacian in RL: Learning Representations with Efficient Approximations ICLR 2019 Bayesian Semi-supervised Learning with Graph Gaussian Processes NeurIPS 2018 Improved large-scale graph learning through ridge spectral sparsification ICML 2018 Label Enhancement for Label Distribution Learning IJCAI 2018 Spectrally Approximating Large Graphs with Smaller Graphs ICML 2018 SpectralNet: Spectral Clustering using Deep Neural Networks ICLR 2018