Uncertainty Quantification & Conformal Prediction Reading List

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

Reliable deployment needs more than accuracy — it needs honest error bars. This set pairs Bayesian and ensemble foundations with calibration diagnostics and modern distribution-free conformal guarantees.

Uncertainty Quantification & Conformal Prediction: 10 key papers

1. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
   Lakshminarayanan et al. arXiv 2016.
2. Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
   Ovadia et al. arXiv 2019.
3. A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
   Angelopoulos and Bates. arXiv 2021.
4. Conformal Risk Control
   Angelopoulos et al. arXiv 2022.
5. Uncertainty Sets for Image Classifiers using Conformal Prediction
   Angelopoulos et al. arXiv 2020.
6. Accurate Uncertainties for Deep Learning Using Calibrated Regression
   Kuleshov et al. arXiv 2018.
7. On Calibration of Modern Neural Networks
   Guo et al. arXiv 2017.
8. Evidential Deep Learning to Quantify Classification Uncertainty
   Sensoy et al. arXiv 2018.
9. Deep Evidential Regression
   Amini et al. arXiv 2019.
10. Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control
    Michelmore et al. arXiv 2019.

Related reading lists

• Bayesian Deep Learning
• Bayesian RL & Thompson Sampling
• Autonomous Driving with RL
• Kernel Methods & RKHS
• Model-Based Reinforcement Learning
• Classical Planning & Search Algorithms
• Active Learning & Adaptive Sampling
• Fairness & Bias in ML