Lightweight, useful implementation of conformal prediction on real data.

Out-of-distribution detection, robustness, and generalization resources. The repository contains a professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc

Collection of awesome test-time (domain/batch/instance) adaptation methods

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

Frouros: an open-source Python library for drift detection in machine learning systems.

A repository and benchmark for online test-time adaptation.

Domain Adaptation for Time Series Under Feature and Label Shifts

A curated list of papers and resources about the distribution shift in machine learning.

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

A graph reliability toolbox based on PyTorch and PyTorch Geometric (PyG).

The official API of DoubleAdapt (KDD'23), an incremental learning framework for online stock trend forecasting, WITHOUT dependencies on the qlib package.

This repository contains the code of the distribution shift framework presented in A Fine-Grained Analysis on Distribution Shift (Wiles et al., 2022).

"Towards Semi-supervised Learning with Non-random Missing Labels" by Yue Duan (ICCV 2023)

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

Library for the training and evaluation of object-centric models (ICML 2022)

[NeurIPS21] TTT++: When Does Self-supervised Test-time Training Fail or Thrive?

[ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"

A python package providing a benchmark with various specified distribution shift patterns.

"Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data" (NeurIPS 21')