Out-of-Distribution & Robustness Reading List

Curated by Mouhssine Rifaki | Stanford Electrical Engineering | Last updated August 2026

Core papers on out-of-distribution detection, robustness, and distribution shift in machine learning.

Out-of-Distribution & Robustness: 10 key papers

1. A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
   Hendrycks and Gimpel. arXiv 2016.
2. Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
   Liang et al. arXiv 2017.
3. A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
   Lee et al. arXiv 2018.
4. Energy-based Out-of-distribution Detection
   Liu et al. arXiv 2020.
5. Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data
   Hsu et al. arXiv 2020.
6. Deep Anomaly Detection with Outlier Exposure
   Hendrycks et al. arXiv 2018.
7. Likelihood Ratios for Out-of-Distribution Detection
   Ren et al. arXiv 2019.
8. Do Deep Generative Models Know What They Don't Know?
   Nalisnick et al. arXiv 2018.
9. Generalized Out-of-Distribution Detection: A Survey
   Yang et al. arXiv 2021.
10. SSD: A Unified Framework for Self-Supervised Outlier Detection
    Sehwag et al. arXiv 2021.

Related reading lists

• Anomaly & Out-of-Distribution Detection
• Adversarial Reinforcement Learning & Robustness to Adversaries
• Certified Robustness
• Active Learning & Adaptive Sampling
• Hindsight Experience Replay & Relabeling
• Policy Gradient Methods
• Bayesian Deep Learning
• Sample Complexity in Reinforcement Learning