{"total":40,"rows":[{"id":"2407.04846","title":"Amazing Things Come From Having Many Good Models","created":"2024-07-05","cited_by_count":55,"area":null},{"id":"2305.11997","title":"Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees","created":"2023-05-19","cited_by_count":45,"area":null},{"id":"2310.19726","title":"A Path to Simpler Models Starts With Noise","created":"2023-10-30","cited_by_count":33,"area":null},{"id":"2302.14517","title":"Arbitrary Decisions are a Hidden Cost of Differentially Private Training","created":"2023-02-28","cited_by_count":19,"area":null},{"id":"2402.00728","title":"Dropout-Based Rashomon Set Exploration for Efficient Predictive Multiplicity Estimation","created":"2024-02-01","cited_by_count":18,"area":null},{"id":"2312.15097","title":"Recourse under Model Multiplicity via Argumentative Ensembling (Technical Report)","created":"2023-12-22","cited_by_count":15,"area":null},{"id":"2402.07745","title":"Predictive Churn with the Set of Good Models","created":"2024-02-12","cited_by_count":15,"area":null},{"id":"2308.07832","title":"REFORMS: Reporting Standards for Machine Learning Based Science","created":"2023-08-15","cited_by_count":13,"area":null},{"id":"2503.01087","title":"Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time","created":"2025-03-03","cited_by_count":13,"area":null},{"id":"2306.13738","title":"Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints","created":"2023-06-23","cited_by_count":11,"area":null},{"id":"2405.01557","title":"An Experimental Study on the Rashomon Effect of Balancing Methods in Imbalanced Classification","created":"2024-03-22","cited_by_count":11,"area":null},{"id":"2501.15634","title":"Be Intentional About Fairness!: Fairness, Size, and Multiplicity in the Rashomon Set","created":"2025-01-26","cited_by_count":10,"area":null},{"id":"2410.22598","title":"Feature Responsiveness Scores: Model-Agnostic Explanations for Recourse","created":"2024-10-29","cited_by_count":8,"area":null},{"id":"2412.18138","title":"What Constitutes a Less Discriminatory Algorithm?","created":"2024-12-24","cited_by_count":8,"area":null},{"id":"2412.09712","title":"Investigating the Impact of Balancing, Filtering, and Complexity on Predictive Multiplicity: A Data-Centric Perspective","created":"2024-12-12","cited_by_count":7,"area":null},{"id":"2507.14744","title":"Beyond the Single-Best Model: Rashomon Partial Dependence Profile for Trustworthy Explanations in AutoML","created":"2025-07-19","cited_by_count":6,"area":null},{"id":"2306.09425","title":"Arbitrariness Lies Beyond the Fairness-Accuracy Frontier","created":"2023-06-15","cited_by_count":5,"area":null},{"id":"2503.07934","title":"Counterfactual Explanations for Model Ensembles Using Entropic Risk Measures","created":"2025-03-11","cited_by_count":5,"area":null},{"id":"2407.17395","title":"The Costs of Pretending That There Are Data-Generating Probability Distributions in the Social World","created":"2024-07-24","cited_by_count":4,"area":null},{"id":"2503.13506","title":"The Role of Hyperparameters in Predictive Multiplicity","created":"2025-03-13","cited_by_count":4,"area":null},{"id":"2504.04243","title":"Perils of Label Indeterminacy: A Case Study on Prediction of Neurological Recovery After Cardiac Arrest","created":"2025-04-05","cited_by_count":3,"area":null},{"id":"2506.04553","title":"Unsupervised Machine Learning for Scientific Discovery: Workflow and Best Practices","created":"2025-06-05","cited_by_count":3,"area":null},{"id":"2402.03481","title":"FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning","created":"2024-02-05","cited_by_count":2,"area":null},{"id":"2502.15772","title":"Rashomon perspective for measuring uncertainty in the survival predictive maintenance models","created":"2025-02-16","cited_by_count":2,"area":null},{"id":"2507.23136","title":"Observational Multiplicity","created":"2025-07-30","cited_by_count":2,"area":null},{"id":"2602.11944","title":"Using predictive multiplicity to measure individual performance within the AI Act","created":"2026-02-12","cited_by_count":2,"area":null},{"id":"2504.09330","title":"Regretful Decisions under Label Noise","created":"2025-04-12","cited_by_count":1,"area":null},{"id":"2504.12156","title":"Predictive Multiplicity in Survival Models: A Method for Quantifying Model Uncertainty in Predictive Maintenance Applications","created":"2025-04-16","cited_by_count":1,"area":null},{"id":"2506.20260","title":"Argumentative Ensembling for Robust Recourse under Model Multiplicity","created":"2025-06-25","cited_by_count":1,"area":null},{"id":"2511.19636","title":"Parameter-Efficient Construction of the Rashomon Slice for Concept Bottleneck Models","created":"2025-11-24","cited_by_count":1,"area":null},{"id":"2601.20627","title":"DIVERSE: Disagreement-Inducing Vector Evolution for Rashomon Set Exploration","created":"2026-01-28","cited_by_count":1,"area":null},{"id":"2603.22750","title":"REALITrees: Rashomon Ensemble Active Learning for Interpretable Trees","created":"2026-03-24","cited_by_count":1,"area":null},{"id":"2608.11541","title":"Robust Ambiguity Detection (RAD) From Model- and Feature-Space Consistency","created":"2026-08-12","cited_by_count":1,"area":null},{"id":"2501.16549","title":"Reconciling Predictive Multiplicity in Practice","created":"2025-01-27","cited_by_count":0,"area":null},{"id":"2507.22556","title":"VAR: Visual Analysis for Rashomon Set of Machine Learning Models' Performance","created":"2025-07-30","cited_by_count":0,"area":null},{"id":"2510.21303","title":"Data as a Lever: A Neighbouring Datasets Perspective on Predictive Multiplicity","created":"2025-10-24","cited_by_count":0,"area":null},{"id":"2602.09520","title":"Rashomon Sets and Model Multiplicity in Federated Learning","created":"2026-02-10","cited_by_count":0,"area":null},{"id":"2603.11701","title":"Decomposing Observational Multiplicity in Decision Trees: Leaf and Structural Regret","created":"2026-03-12","cited_by_count":0,"area":null},{"id":"2603.11750","title":"Mitigating the Multiplicity Burden: The Role of Calibration in Reducing Predictive Multiplicity of Classifiers","created":"2026-03-12","cited_by_count":0,"area":null},{"id":"2609.36970","title":"Equally Good, Yet Different: Benchmarking Rashomon sets in AutoML packages","created":"2026-09-29","cited_by_count":0,"area":null}],"next_cursor":null}