SoheilFeizi / spectral-graph-alignmentLinks
Spectral Alignment of Graphs
☆11Updated 8 years ago
Alternatives and similar repositories for spectral-graph-alignment
Users that are interested in spectral-graph-alignment are comparing it to the libraries listed below
Sorting:
- Lightweight Python library for in-memory matrix completion.☆107Updated 2 years ago
- TensorFlow implementation of 'Core', proposed in "Conditional Variance Penalties and Domain Shift Robustness".☆10Updated 6 years ago
- Implementation of the Multiscale Laplacian Graph Kernel☆19Updated 5 years ago
- Graph matching and clustering by comparing heat kernels via optimal transport.☆27Updated 2 years ago
- Conditional Independence Testing with Generative Adversarial Networks☆12Updated 5 years ago
- Deep Large-Scale Inference UsingKnockoffs☆11Updated 3 years ago
- Code to reproduce the numerical experiments in the paper Domain adaptation under structural causal models by Yuansi Chen and Peter Bühlma…☆18Updated 4 years ago
- D-VAE: A Variational Autoencoder for Directed Acyclic Graphs, NeurIPS 2019☆141Updated 4 years ago
- learning point processes by means of optimal transport and wasserstein distance☆54Updated 7 years ago
- Code for the Structural Agnostic Model (https://arxiv.org/abs/1803.04929)☆53Updated 4 years ago
- ☆40Updated 6 years ago
- Codebase for INVASE: Instance-wise Variable Selection - 2019 ICLR☆62Updated 5 years ago
- Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching☆44Updated 5 years ago
- Code and data for the paper `Bayesian Semi-supervised Learning with Graph Gaussian Processes'☆38Updated 6 years ago
- Classifier Conditional Independence Test: A CI test that uses a binary classifier (XGBoost) for CI testing☆45Updated 2 years ago
- Approximate knockoffs and model-free variable selection.☆55Updated 3 years ago
- The code for the paper *The Sensitivity of Counterfactual Fairness to Unmeasured Confounding* @ UAI 2019☆12Updated 5 years ago
- Graph Learning from Data☆35Updated 5 years ago
- A fast MATLAB implementation of the Weisfeiler--Lehman graph transformation and associated kernel.☆11Updated 7 years ago
- Replication code for the article "Learning Functional Causal Models with Generative Neural Networks"☆100Updated 6 years ago
- ☆32Updated 7 years ago
- Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper☆36Updated 5 years ago
- Implementation of Graph Neural Tangent Kernel (NeurIPS 2019)☆105Updated 5 years ago
- 🤖🤖 Attentive Mixtures of Experts (AMEs) are neural network models that learn to output both accurate predictions and estimates of featu…☆42Updated 2 years ago
- A Wasserstein Subsequence Kernel for Time Series.☆21Updated last year
- ☆96Updated 2 years ago
- Wasserstein Weisfeiler-Lehman Graph Kernels☆83Updated last year
- Code for `BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery`, Neurips 2021☆26Updated 3 years ago
- Code for ICML 2021 paper "Regularizing towards Causal Invariance: Linear Models with Proxies" (ICML 2021)☆11Updated 3 years ago
- Repository for "Differentiable Causal Discovery from Interventional Data"☆76Updated 3 years ago