Research map / Learning theory and algorithms / Prediction and decision losses
Compact interpretable model classes
Learn small interpretable models such as diagrams, lattices and oblique trees.
26 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026) · an approach in Prediction and decision losses, keyword cluster “trees · decision tree · forests”
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.
5 papers in 2023–24 and 4 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic shrank, from 0.9% to 0.7%.
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 · The Global Patch Collider (CVPR 2016 · 38 citations)
- 2019 · Deep Neural Networks Constrained by Decision Rules (AAAI 2019 · 13 citations)
- 2020 · ADDMC: Weighted Model Counting with Algebraic Decision Diagrams (AAAI 2020 · 30 citations)
- 2021 · Neural Additive Models: Interpretable Machine Learning with Neural Nets (NeurIPS 2021 · 121 citations)
- 2022 · Optimizing Binary Decision Diagrams with MaxSAT for Classification (AAAI 2022 · 12 citations)
- 2023 · MLFMF: Data Sets for Machine Learning for Mathematical Formalization (NeurIPS 2023 · 2 citations)
- 2024 · Using Noise to Infer Aspects of Simplicity Without Learning (NeurIPS 2024 · 1 citation)
- 2025 · What makes an Ensemble (Un) Interpretable? (ICML 2025 · 0 citations)
- 2026 · Counterfactual Maps: What They Are and How to Find Them (NeurIPS 2026 · 0 citations)
Earliest papers here
- The Global Patch Collider (CVPR 2016 · 38 citations)
- Fast and Flexible Monotonic Functions with Ensembles of Lattices (NeurIPS 2016 · 28 citations)
- Zero-Suppressed Sentential Decision Diagrams (AAAI 2016 · 22 citations)
What has been tried
24 papers here report 205 measured changes (a component added, removed or swapped, compared with a baseline): helped 56%, no clear effect 13%, hurt 27%, mixed 4%. Papers mostly publish changes that helped, so "helped" is over-represented.
Ablations in recent papers
| What was changed | Measured on | Result | Effect | Paper |
|---|---|---|---|---|
| Set the regularization parameter to α=0 instead of α=∞ | ADNI · AUROC | Helped | Prediction models that learn to avoid missing values (ICML 2025 · 0 citations) | |
| Use DiCE counterfactuals instead of OCEAN counterfactuals | Decision trees and random forests · fidelity | Helped | From Counterfactuals to Trees: Competitive Analysis of Model Extraction Attacks (NeurIPS 2025 · 0 citations) |
What reviewers will expect
Datasets and baselines named in the 6 papers of this approach from 2024–2026 that have a full-text summary. Leaving out the common ones is a frequent review question.
Datasets
- FICO · 3 of 6 papers
- COMPAS · 3 of 6 papers
- German Credit · 2 of 6 papers
Recent preprints
Loading recent arXiv preprints…
Who publishes on it
Institutions on the most papers of this approach since 2023 (of 9 papers; from OpenAlex and the papers' affiliations).
- École Polytechnique de Montréal · 2 papers
Use it
Download BibTeX of 25 key papersOpen the topic in the interactive map
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Related approaches
Same keyword cluster
- Tree ensemble optimization (77 papers)
- Optimal decision tree learning (75 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)
- Conformal prediction sets (116 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).