Velodrome combines semi-supervised learning and out-of-distribution generalization (domain generalization) for drug response prediction and pharmacogenomics

Codes and datasets for NeurIPS21 paper “Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach”

GradCAM-based Copy and Paste Augmentation

Multi-Domain Balanced Sampling Improves Out-of-Distribution Generalization of Chest X-ray Pathology Prediction Models

This work is a analysis of representations acquired for standard, OOD and Biased data on numerous objective functions.

The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"

Codes and datasets for ICML21 paper "Towards open-world recommendation: An inductive model-based collaborative filtering approach"

Official implementation for the paper "Learning Substructure Invariance for Out-of-Distribution Molecular Representations" (NeurIPS 2022).

The Pytorch implementation for "Topology-aware Robust Optimization for Out-of-Distribution Generalization" (ICLR 2023)

Potential energy ranking for domain generalization (DG)

Papers about out-of-distribution generalization on graphs.

The official implementation for ICLR23 paper "GNNSafe: Energy-based Out-of-Distribution Detection for Graph Neural Networks"

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

[NeurIPS 2023] “SODA: Robust Training of Test-Time Data Adaptors”

Official PyTorch implementation of the ICCV'23 paper “Anomaly Detection under Distribution Shift”

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

[NeurIPS 2022] The official repository of Expression Learning with Identity Matching for Facial Expression Recognition

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

Code for the research paper Meta-learning with hierarchical models based on similarity of causal mechanisms