GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]

Official code of "Discovering Invariant Rationales for Graph Neural Networks" (ICLR 2022)

[NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs

[KDD'2023] "KGRec: Knowledge Graph Self-Supervised Rationalization for Recommendation"

Tools for exploiting Morphological Symmetries in robotics

[ICLR 2023, ICLR DG oral] PAIR, the optimizer and model selection criteria for OOD Generalization

Ratioanle-aware Graph Contrastive Learning codebase

[NeurIPS 2023] Understanding and Improving Feature Learning for Out-of-Distribution Generalization

This repo contains code for Invariant Grounding for Video Question Answering

[NeurIPS 2023] Does Invariant Graph Learning via Environment Augmentation Learn Invariance?

[ICLR'25 Spotlight] Revisiting Random Walks for Learning on Graphs, in PyTorch

Causal Disentangled Recommendation Against Preference Shifts (TOIS), 2023

This repository provides implementations for sparse graph separation, power graph experiments, and chemical property prediction.

Code for "Environment Diversification with Multi-head Neural Network for Invariant Learning" (NeurIPS 2022)

This repository contain code for research paper "Causal Discovery via Intrinsic invariant Conditional Probabilities"

DELA - Disentanglement Learning Archive

Basic Invariant Test Practice in Foundry