nickruggeri / CLAP-interpretable-predictionsLinks
Official codebase for the paper "Provable concept learning for interpretable predictions using variational inference".
☆14Updated 3 years ago
Alternatives and similar repositories for CLAP-interpretable-predictions
Users that are interested in CLAP-interpretable-predictions are comparing it to the libraries listed below
Sorting:
- Code for the paper "Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)" (2020)☆32Updated 3 years ago
- ☆17Updated 2 years ago
- ☆23Updated 3 years ago
- Code for "Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties"☆18Updated 4 years ago
- Disentangled gEnerative cAusal Representation (DEAR)☆60Updated 2 years ago
- VAEs and nonlinear ICA: a unifying framework☆37Updated 5 years ago
- Code for ICE-BeeM paper - NeurIPS 2020☆87Updated 3 years ago
- Code for Diff-SCM paper☆97Updated 2 years ago
- This repository contains the implementation of Concept Activation Regions, a new framework to explain deep neural networks with human con…☆12Updated 2 years ago
- Diffusion Models for Causal Discovery☆85Updated 2 years ago
- Efficient Conditionally Invariant Representation Learning (ICLR 2023, Oral)☆21Updated 2 years ago
- ☆51Updated last year
- VAEs and nonlinear ICA: a unifying framework☆47Updated 6 years ago
- Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations is a ServiceNow Research project that was started at Elemen…☆13Updated 2 years ago
- ☆17Updated last year
- Code for "Causal autoregressive flows" - AISTATS, 2021☆45Updated 4 years ago
- Repository for our NeurIPS 2022 paper "Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off" and our NeurIPS 2023 paper…☆64Updated 2 months ago
- Official PyTorch implementation of 🏁 MFCVAE 🏁: "Multi-Facet Clustering Variatonal Autoencoders (MFCVAE)" (NeurIPS 2021). A class of var…☆40Updated last year
- ☆25Updated last year
- Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts (Neurips 2020)☆77Updated 3 years ago
- The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).☆35Updated 4 years ago
- Python code of Hilbert-Schmidt Independence Criterion☆87Updated 3 years ago
- Mutual information estimators and benchmark☆51Updated 6 months ago
- Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"☆35Updated last year
- Implementation of the Gromov-Wasserstein distance to the setting of Unbalanced Optimal Transport☆45Updated 2 years ago
- Official repository of the paper "Efficient Neural Causal Discovery without Acyclicity Constraints"☆84Updated last year
- Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations (ICML 2022)☆27Updated 3 years ago
- Code used in the paper "Score matching enables causal discovery of nonlinear additive noise models", Rolland et al., ICML 2022☆19Updated 3 years ago
- Code for "Generative causal explanations of black-box classifiers"☆34Updated 4 years ago
- Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style☆53Updated 3 years ago