mtsang / neural-interaction-detection
Detecting Statistical Interactions from Neural Network Weights
☆47Updated 4 years ago
Alternatives and similar repositories for neural-interaction-detection:
Users that are interested in neural-interaction-detection are comparing it to the libraries listed below
- Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.☆30Updated 5 years ago
- Code for the Structural Agnostic Model (https://arxiv.org/abs/1803.04929)☆52Updated 4 years ago
- ☆90Updated last year
- Codebase for INVASE: Instance-wise Variable Selection - 2019 ICLR☆60Updated 4 years ago
- Repository for "Differentiable Causal Discovery from Interventional Data"☆72Updated 3 years ago
- Repository for code release of paper "Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data" (AISTATS 2020)☆50Updated 4 years ago
- Classifier Conditional Independence Test: A CI test that uses a binary classifier (XGBoost) for CI testing☆46Updated last year
- ☆92Updated last year
- ☆124Updated 3 years ago
- Local explanations with uncertainty 💐!☆39Updated last year
- Tools for training explainable models using attribution priors.☆122Updated 3 years ago
- Feature Interaction Interpretability via Interaction Detection☆34Updated last year
- Codebase for "Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains Its Predictions" (to appear in AA…☆74Updated 7 years ago
- MisGAN: Learning from Incomplete Data with GANs☆80Updated last year
- Implementation of the MIWAE method for deep generative modelling of incomplete data sets.☆37Updated 11 months ago
- ☆32Updated 6 years ago
- Classifier based mutual information, conditional mutual information estimation; conditional independence testing☆34Updated 5 years ago
- A Pytorch implementation of missing data imputation using optimal transport.☆96Updated 3 years ago
- On the Role of Sparsity and DAG Constraints for Learning Linear DAGs☆33Updated 3 years ago
- Approximate knockoffs and model-free variable selection.☆52Updated 3 years ago
- ☆32Updated 2 years ago
- This is the source code for Learning Deep Kernels for Non-Parametric Two-Sample Tests (ICML2020).☆48Updated 3 years ago
- learning point processes by means of optimal transport and wasserstein distance☆54Updated 7 years ago
- Code for the paper: Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data☆207Updated 2 years ago
- Pytorch implementation of SOM-VAE: INTERPRETABLE DISCRETE REPRESENTATION LEARNING ON TIME SERIES https://arxiv.org/pdf/1806.02199v7.pdf☆31Updated 5 years ago
- Fair Empirical Risk Minimization (FERM)☆37Updated 4 years ago
- DeepPINK: reproducible feature selection in deep neural networks☆21Updated last year
- TensorFlow implementation of the SOM-VAE model as described in https://arxiv.org/abs/1806.02199☆194Updated last year
- A benchmark for distribution shift in tabular data☆50Updated 8 months ago
- Replication code for the article "Learning Functional Causal Models with Generative Neural Networks"☆99Updated 5 years ago