Research map / Learning theory and algorithms / Prediction and decision losses
Probabilistic calibration
Calibrate classifier and regression uncertainty with proper scores and calibration guarantees.
55 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2018–2026) · an approach in Prediction and decision losses, keyword cluster “conformal · predictions · online”
State History Tried Checklist 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.
19 papers in 2023–24 and 18 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic shrank, from 3.6% to 3.1%.
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
- 2018 · Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time (ICML 2018 · 2 citations)
- 2019 · Verified Uncertainty Calibration (NeurIPS 2019 · 138 citations)
- 2020 · PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction (ICLR 2020 · 21 citations)
- 2021 · Uncertainty Sets for Image Classifiers using Conformal Prediction (ICLR 2021 · 31 citations)
- 2022 · Better Uncertainty Calibration via Proper Scores for Classification and Beyond (NeurIPS 2022 · 11 citations)
- 2023 · Object Pose Estimation With Statistical Guarantees: Conformal Keypoint Detection and Geometric Uncertainty Propagation (CVPR 2023 · 32 citations)
- 2024 · Active, anytime-valid risk controlling prediction sets (NeurIPS 2024 · 4 citations)
- 2025 · SAFER: A Calibrated Risk-Aware Multimodal Recommendation Model for Dynamic Treatment Regimes (ICML 2025 · 0 citations)
- 2026 · COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees (AAAI 2026 · 1 citation)
Earliest papers here
- Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time (ICML 2018 · 2 citations)
- Approximation Algorithms for Cascading Prediction Models (ICML 2018 · 0 citations)
- Verified Uncertainty Calibration (NeurIPS 2019 · 138 citations)
What has been tried
40 papers here report 353 measured changes (a component added, removed or swapped, compared with a baseline): helped 65%, no clear effect 10%, hurt 18%, mixed 7%. Papers mostly publish changes that helped, so "helped" is over-represented.
Ablations in recent papers
| What was changed | Measured on | Result | Effect | Paper |
|---|---|---|---|---|
| Add the width regularizer to RQR | Truncated Gaussian noise · Width | 1.72 (±0.010) -> 1.50 (±0.005) | Helped | Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise (ICML 2024 · 1 citation) |
| Localization of threshold via kernel length scale (smaller l) | Elec2 weekend vs weekday coverage · weekend miscoverage error balance | Helped | Localized Adaptive Risk Control (NeurIPS 2024 · 1 citation) | |
| add risk-aware fine-tuning | MIMIC-III · MA-AUC | 0.7140 -> 0.8672 | Helped | SAFER: A Calibrated Risk-Aware Multimodal Recommendation Model for Dynamic Treatment Regimes (ICML 2025 · 0 citations) |
| Use KM-sampling instead of deterministic decensoring methods | Medium to large datasets · Calibration performance | Helped | Conformalized Survival Distributions: A Generic Post-Process to Increase Calibration (ICML 2024 · 0 citations) | |
| Use ACP-GN (split + refine) rather than ACP-GN without splitting and refinement | UCI datasets · coverage validity | Helped | Approximating Full Conformal Prediction for Neural Network Regression with Gauss-Newton Influence (ICLR 2025) |
What reviewers will expect
Datasets and baselines named in the 15 papers of this approach from 2025–2026 that have a full-text summary. Leaving out the common ones is a frequent review question.
Datasets
- Concrete · 2 of 15 papers
- Community · 2 of 15 papers
- Bike · 2 of 15 papers
Recent preprints
Loading recent arXiv preprints…
Who publishes on it
Institutions on the most papers of this approach since 2023 (of 37 papers; from OpenAlex and the papers' affiliations).
- Carnegie Mellon University · 3 papers
- University of California, Berkeley · 3 papers
- Georgia Institute of Technology · 2 papers
- University of Pennsylvania · 2 papers
- University of Alberta · 2 papers
Use it
Download BibTeX of 39 key papersOpen the topic in the interactive map
Get alerts. Describe your idea in scime atlas and save it (free scime account): you get a weekly email when new preprints land close to it.
Related approaches
Same keyword cluster
- Conformal prediction sets (116 papers)
- Algorithms with explicit predictors (62 papers)
Elsewhere in the topic
- Adaptive testing and experiments (201 papers)
- Active learning and subset selection (199 papers)
- Distributionally robust optimization (184 papers)
- Data attribution and recourse (160 papers)
- Theory of neural network sensitivity (143 papers)
- Ranking models from comparisons (127 papers)
- Determinantal point process inference (122 papers)
- Provable post-hoc explanations (92 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).