ArticleAdvances in neural information processing systems2022
Exploring the Whole Rashomon Set of Sparse Decision Trees.
Article in Advances in neural information processing systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Risk Assessment in Peacekeeping: Are Visual Sparse Models Transparent?Risk analysis : an official publication of the Society for Risk Analysis · 2026Article
- Interpretable machine learning for low-sample multi-omics: a case study of ferret vaccine response.Bioinformatics advances · 2026Article
- Machine learning based multimodel prediction of poor wellbeing, generalized anxiety disorder, and insomnia among adults in Indian urban informal settlements: toward explainable digital mental health screening.Frontiers in digital health · 2026Article
- Article
- Predicting Affinity Through Homology (PATH): Interpretable binding affinity prediction with persistent homology.PLoS computational biology · 2025Article
- A Path to Simpler Models Starts With Noise.Advances in neural information processing systems · 2023Article
- Exploring and Interacting with the Set of Good Sparse Generalized Additive Models.Advances in neural information processing systems · 2023Article
- Augmenting interpretable models with large language models during training.Nature communications · 2023Article
- Decision trees: from efficient prediction to responsible AI.Frontiers in artificial intelligence · 2023Review
- Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design.CEUR workshop proceedings · 2022Article
- Fast Sparse Decision Tree Optimization via Reference Ensembles.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2022Article
- Exploring the Whole Rashomon Set of Sparse Decision Trees.Advances in neural information processing systems · 2022Article
- Mathematical optimization in classification and regression trees.Top (Berlin, Germany) · 2021Article
- A United States Fair Lending Perspective on Machine Learning.Frontiers in artificial intelligence · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
In any given machine learning problem, there might be many models that explain the data almost equally well. However, most learning algorithms return only one of these models, leaving practitioners with no practical way to explore alternative models that might have desirable properties beyond what could be expressed by a loss function. The
Identifiers
37786624PMC10544768What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.