Sharon Yixuan Li

Dan Hendrycks

Thomas G. Dietterich

Balaji Lakshminarayanan

Yarin Gal

Martin Arjovsky

Pang Wei Koh

Peng Cui

Jie Ren

Alexander Meinke

Jingkang Yang

Kimin Lee

Yao Qin

Kaiyang Zhou

David Lopez-Paz

Vaishnavh Nagarajan

(2022) Data Distribution Shifts and Monitoring

by Chip Huyen

(2023) OpenOOD v1.5 Methods & Benchmarks Overview

by the OpenOOD team

(2020) Adapting on the Fly to Test-Time Distribution Shift

by BAIR

(2022) Keeping Learning-Based Control Safe by Regulating Distributional Shift

by BAIR

(2021) Machine Learning Under Distributional Shifts

by Stanford AI Lab

(2024) NeurIPS Tutorial: Out-of-Distribution Generalization: Shortcuts, Spuriousness, and Stability

(2024) Lec 17. Generalization: Out-of-Distribution (OOD)

by Phillip Isola, Sara Beery, and Jeremy Bernstein

(2024) Intro to Out-of-Distribution Detection

by Sharon Yixuan Li

(2023) How to Detect Out-of-Distribution Data in the Wild?

by Sharon Yixuan Li

(2022) Anomaly Detection for OOD and Novel Category Detection

by Thomas G. Dietterich

(2022) Reliable Open-World Learning Against Out-of-Distribution Data

by Sharon Yixuan Li

(2022) Challenges and Opportunities in Out-of-Distribution Detection

by Sharon Yixuan Li

(2022) Exploring the Limits of Out-of-Distribution Detection in Vision and Biomedical Applications

by Jie Ren

(2021) Understanding the Failure Modes of Out-of-distribution Generalization

by Vaishnavh Nagarajan

(2020) Practical Uncertainty Estimation and Out-of-Distribution Robustness in Deep Learning

by Dustin Tran, Balaji Lakshminarayanan, and Jasper Snoek

(2023) OpenOOD v1.5 Methods & Benchmarks Overview

by the OpenOOD team

OpenOOD-VLM

benchmark suite for generalized OOD detection in the vision-language model setting

WILDS

canonical real-world benchmark for distribution shift across vision, text, graphs, and biology

DomainBed

standard evaluation suite for domain generalization and out-of-domain robustness

GOOD

leading benchmark suite for graph out-of-distribution and graph domain generalization

DrugOOD

benchmark and platform for out-of-distribution generalization in AI-aided drug discovery

TableShift

benchmark and toolkit for real-world tabular distribution shift

OODRobustBench

benchmark for adversarial robustness under natural distribution shift

OOD NLP

benchmark suite for out-of-distribution robustness and evaluation in NLP

NINCO

ImageNet-scale near-OOD dataset for modern large-scale visual evaluation

WOODS

benchmark suite for out-of-distribution generalization in time-series tasks

OpenMIBOOD

medical imaging benchmark suite for OOD detection under covariate, near-OOD, and far-OOD shifts

Semantic Shift Benchmark (SSB)

benchmark for semantic-shift, open-set, and class-level OOD evaluation

(2023) OpenOOD v1.5 Methods & Benchmarks Overview

by the OpenOOD team

PyTorch Out-of-Distribution Detection

practical PyTorch library with detectors, losses, datasets, and evaluation utilities

OODEEL

compact post-hoc OOD toolkit for TensorFlow and PyTorch image classifiers

TorchUncertainty

broader uncertainty framework with strong support for OOD metrics, evaluation, and tutorials

Alibi Detect

high-quality toolkit for outlier, adversarial, and drift detection across modalities

Martin Arjovsky

(2023) Robust Out-of-Distribution Detection in Deep Classifiers

by Alexander Meinke

(2023) Detecting and Learning Out-of-Distribution Data in the Open-world: Algorithm and Theory

by Yiyou Sun

(2024) Towards Reliable Foundation Models in the Open World

by Yifei Ming

(2025) Foundations of Unknown-aware Machine Learning

by Xuefeng Du

(2025) Learning to Generalize Across Distribution Shifts

by Frederik Joshua Träuble

(2026) Out-of-Distribution Detection, Sharpness, and Unlearning: Advancing Robust and Trustworthy Deep Learning

by Maximilian Peter Müller