imtiazziko / Variational-Fair-Clustering
Variational Fair clustering
☆11Updated 7 months ago
Alternatives and similar repositories for Variational-Fair-Clustering:
Users that are interested in Variational-Fair-Clustering are comparing it to the libraries listed below
- ☆32Updated 6 years ago
- ☆20Updated 3 years ago
- Gromov-Wasserstein Factorization Models for Graph Clustering (AAAI-20)☆31Updated 2 years ago
- Uncertainty Aware Semi-Supervised Learning on Graph Data☆40Updated 3 years ago
- Codes for NIPS 2019 Paper: Rethinking Kernel Methods for Node Representation Learning on Graphs☆34Updated 4 years ago
- Implementation of Graph Neural Tangent Kernel (NeurIPS 2019)☆103Updated 5 years ago
- Adaptive and Reliable Classification: efficient conformity scores for multi-class classification problems☆31Updated 2 years ago
- Resources and environment for unsupervised outlier model selection (UOMS)☆23Updated 2 years ago
- Official implementation of our FLAG paper (CVPR2022)☆141Updated 2 years ago
- Code for the Structural Agnostic Model (https://arxiv.org/abs/1803.04929)☆52Updated 4 years ago
- NeurIPS 2022: Tree Mover’s Distance: Bridging Graph Metrics and Stability of Graph Neural Networks☆36Updated last year
- Pytorch Implementation of Graph Convolutional Kernel Networks☆54Updated 2 years ago
- Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.☆30Updated 5 years ago
- Source code of "What Makes Graph Neural Networks Miscalibrated?" (NeurIPS 2022)☆21Updated last year
- Code for reproducing results in GraphMix paper☆72Updated 2 years ago
- Code for Graph Neural Networks Exponentially Lose Expressive Power for Node Classification.☆30Updated 4 years ago
- Codebase for "Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains Its Predictions" (to appear in AA…☆73Updated 7 years ago
- Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport☆36Updated 3 years ago
- The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).☆31Updated 4 years ago
- Codebase for "Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions", ICML 2020.☆8Updated 4 years ago
- Contains the code relative to the paper Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning https://arxiv.org/abs…☆21Updated 4 years ago
- The official implementation of the Graph Barlow Twins method with the experimental pipeline☆30Updated last year
- Gromov-Wasserstein Learning for Graph Matching and Node Embedding☆72Updated 5 years ago
- Causal Effect Inference for Structured Treatments (SIN) (NeurIPS 2021)☆42Updated 2 years ago
- Correlated Graph Neural Networks☆26Updated 4 years ago
- Non-Parametric Calibration for Classification (AISTATS 2020)☆19Updated 2 years ago
- Software and pre-processed data for "Using Embeddings to Correct for Unobserved Confounding in Networks"☆55Updated last year
- Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."☆65Updated 2 years ago
- Implementation of the paper "Certifiable Robustness and Robust Training for Graph Convolutional Networks".☆42Updated 4 years ago
- Code for Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding☆21Updated 2 years ago