Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Bayesian decision-making under uncertainty
Applies Bayesian modeling and distillation to decisions and measurement under model uncertainty.
57 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026) · an approach in Uncertainty and Gaussian processes, keyword cluster “uncertainty estimation · epistemic · quantification”
State History Tried Preprints Who Export
Where it stands
Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.
18 papers in 2023–24 and 19 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic held steady, from 6.6% to 6.2%.
How this is decided
A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.
How it got here
Most cited paper of each year
- 2016 · A Proactive Sampling Approach to Project Scheduling under Uncertainty (AAAI 2016 · 17 citations)
- 2017 · Counting-Based Reliability Estimation for Power-Transmission Grids (AAAI 2017 · 45 citations)
- 2019 · Probabilistic Model Checking of Robots Deployed in Extreme Environments (AAAI 2019 · 23 citations)
- 2020 · Probabilistic Regression for Visual Tracking (CVPR 2020 · 775 citations)
- 2021 · Deep Probabilistic Imaging: Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imaging (AAAI 2021 · 39 citations)
- 2022 · Counterfactual Plans under Distributional Ambiguity (ICLR 2022 · 4 citations)
- 2023 · Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty (AAAI 2023 · 21 citations)
- 2024 · Producing and Leveraging Online Map Uncertainty in Trajectory Prediction (CVPR 2024 · 37 citations)
- 2025 · DutyTTE: Deciphering Uncertainty in Origin-Destination Travel Time Estimation (AAAI 2025 · 8 citations)
- 2026 · Interventional Processes For Causal Uncertainty Quantification (ICML 2026 · 0 citations)
Earliest papers here
- A Proactive Sampling Approach to Project Scheduling under Uncertainty (AAAI 2016 · 17 citations)
- Counting-Based Reliability Estimation for Power-Transmission Grids (AAAI 2017 · 45 citations)
- Probabilistic Model Checking of Robots Deployed in Extreme Environments (AAAI 2019 · 23 citations)
What has been tried
42 papers here report 376 measured changes (a component added, removed or swapped, compared with a baseline): helped 72%, no clear effect 8%, hurt 16%, mixed 5%. Papers mostly publish changes that helped, so "helped" is over-represented.
Ablations in recent papers
| What was changed | Measured on | Result | Effect | Paper |
|---|---|---|---|---|
| Add map uncertainty to MapTR map inputs | nuScenes · minADE | 0.4015 -> 0.3854 | Helped | Producing and Leveraging Online Map Uncertainty in Trajectory Prediction (CVPR 2024 · 37 citations) |
| add MoE-guided uncertainty quantification | Chengdu · PICP | 88.63 -> 91.02 | Helped | DutyTTE: Deciphering Uncertainty in Origin-Destination Travel Time Estimation (AAAI 2025 · 8 citations) |
| Use hierarchical rather than flat clustering | Toy denoising · representation of rare posterior modes | Helped | Hierarchical Uncertainty Exploration via Feedforward Posterior Trees (NeurIPS 2024 · 4 citations) | |
| Increase dropout rate from 0.05 to 0.20 | Tumor CT · DSC (Full Model, % change) | +1.21% -> -2.59% | Hurt | Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation (CVPR 2025 · 3 citations) |
| Add three-fold cross-validation refinement to sample splitting | Synthetic Gaussian, SNR=0.9, K_n=15, α=10% · P_+ | 2.5 -> 0.0 | Helped | Uncertainty Quantification for Data-Driven Change-Point Learning via Cross-Validation (AAAI 2024 · 3 citations) |
| filter the 5% least confident predictions | Norman, Rep1, and K562 · Pearson correlation | Norman: 58.38 -> 61.58; Rep1: 59.18 -> 60.20; K562: 36.20 -> 38.28 | Helped | PRESCRIBE: Predicting Single-Cell Responses with Bayesian Estimation (NeurIPS 2025 · 0 citations) |
| Add a structured hard-region constraint instead of using the global robustness constraint | Toy example · ESS | 1491 -> 1495 | Helped | Robust Sampling for Active Statistical Inference (NeurIPS 2025 · 0 citations) |
| Use learned uncertainty estimates instead of ground-truth uncertainty | ProcGen Maze test domains · success rate | No clear effect | A Bayesian Approach to Online Planning (ICML 2024 · 0 citations) | |
| Add DAB to TNP | 2D RBF GP, EMNIST, and CelebA targets · log-likelihood | 0.362 -> 0.365; 0.945 -> 0.949; 1.632 -> 1.645 | Helped | Dimension Agnostic Neural Processes (ICLR 2025) |
| replace the Dirichlet prior with a continuous categorical prior | MNIST · accuracy | 0.980 -> 0.979 | No clear effect | Bayesian Knowledge Distillation: A Bayesian Perspective of Distillation with Uncertainty Quantification (ICML 2024) |
Recent preprints
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Who publishes on it
Institutions on the most papers of this approach since 2023 (of 37 papers; from OpenAlex and the papers' affiliations).
- Stanford University · 3 papers
- Carnegie Mellon University · 2 papers
- University of Virginia · 2 papers
- Technion - Israel Institute of Technology · 2 papers
- Technion · 2 papers
- Amazon · 2 papers
- Shanghai Jiao Tong University · 2 papers
Use it
Download BibTeX of 35 key papersOpen the topic in the interactive map
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Related approaches
Same keyword cluster
- Priors and functional variational uncertainty (112 papers)
- Single-model predictive uncertainty (74 papers)
- Epistemic versus aleatoric decomposition (75 papers)
Elsewhere in the topic
- Nonparametric and PAC-Bayes Bayesian learning (130 papers)
- Simulation-based inference (118 papers)
- Variational Bayesian deep learning (105 papers)
- Approximate inference in Bayesian neural networks (93 papers)
- Sparse variational Gaussian processes (92 papers)
- PAC-Bayes and decoupled GP learning (83 papers)
- Bayesian view of meta-learning (63 papers)
- Infinite-width networks as Gaussian processes (49 papers)
Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).