Policy Trees for Prediction: Interpretable and Adaptive Model Selection for Machine Learning
Fuente:
arXiv
Guardado en:
| Autores principales: | Bertsimas, Dimitris, Peroni, Matthew |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Deep Trees for (Un)structured Data: Tractability, Performance, and Interpretability
por: Bertsimas, Dimitris, et al.
Publicado: (2024)
por: Bertsimas, Dimitris, et al.
Publicado: (2024)
Prescribe-then-Select: Adaptive Policy Selection for Contextual Stochastic Optimization
por: Iglesias, Caio de Prospero, et al.
Publicado: (2025)
por: Iglesias, Caio de Prospero, et al.
Publicado: (2025)
A Machine Learning Approach to Two-Stage Adaptive Robust Optimization
por: Bertsimas, Dimitris, et al.
Publicado: (2023)
por: Bertsimas, Dimitris, et al.
Publicado: (2023)
Adaptive Optimization for Prediction with Missing Data
por: Bertsimas, Dimitris, et al.
Publicado: (2024)
por: Bertsimas, Dimitris, et al.
Publicado: (2024)
Global Optimization: A Machine Learning Approach
por: Bertsimas, Dimitris, et al.
Publicado: (2023)
por: Bertsimas, Dimitris, et al.
Publicado: (2023)
Adaptive Forests For Classification
por: Bertsimas, Dimitris, et al.
Publicado: (2025)
por: Bertsimas, Dimitris, et al.
Publicado: (2025)
M3H: Multimodal Multitask Machine Learning for Healthcare
por: Bertsimas, Dimitris, et al.
Publicado: (2024)
por: Bertsimas, Dimitris, et al.
Publicado: (2024)
From Data to Uncertainty Sets: a Machine Learning Approach
por: Bertsimas, Dimitris, et al.
Publicado: (2025)
por: Bertsimas, Dimitris, et al.
Publicado: (2025)
Optimal Control of Multiclass Fluid Queueing Networks: A Machine Learning Approach
por: Bertsimas, Dimitris, et al.
Publicado: (2023)
por: Bertsimas, Dimitris, et al.
Publicado: (2023)
Overfitting in Adaptive Robust Optimization
por: Zhu, Karl, et al.
Publicado: (2025)
por: Zhu, Karl, et al.
Publicado: (2025)
Catastrophe Insurance: An Adaptive Robust Optimization Approach
por: Bertsimas, Dimitris, et al.
Publicado: (2024)
por: Bertsimas, Dimitris, et al.
Publicado: (2024)
Predictive Low Rank Matrix Learning under Partial Observations: Mixed-Projection ADMM
por: Bertsimas, Dimitris, et al.
Publicado: (2024)
por: Bertsimas, Dimitris, et al.
Publicado: (2024)
Optimal Control of Fluid Restless Multi-armed Bandits: A Machine Learning Approach
por: Bertsimas, Dimitris, et al.
Publicado: (2025)
por: Bertsimas, Dimitris, et al.
Publicado: (2025)
Simple Imputation Rules for Prediction with Missing Data: Contrasting Theoretical Guarantees with Empirical Performance
por: Bertsimas, Dimitris, et al.
Publicado: (2021)
por: Bertsimas, Dimitris, et al.
Publicado: (2021)
Binary Classification: Is Boosting stronger than Bagging?
por: Bertsimas, Dimitris, et al.
Publicado: (2024)
por: Bertsimas, Dimitris, et al.
Publicado: (2024)
Multimodal Prescriptive Deep Learning
por: Bertsimas, Dimitris, et al.
Publicado: (2025)
por: Bertsimas, Dimitris, et al.
Publicado: (2025)
Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences
por: Bertsimas, Dimitris, et al.
Publicado: (2024)
por: Bertsimas, Dimitris, et al.
Publicado: (2024)
Algorithmic Insurance
por: Bertsimas, Dimitris, et al.
Publicado: (2021)
por: Bertsimas, Dimitris, et al.
Publicado: (2021)
Robust Regression over Averaged Uncertainty
por: Bertsimas, Dimitris, et al.
Publicado: (2023)
por: Bertsimas, Dimitris, et al.
Publicado: (2023)
Compressed Sensing: A Discrete Optimization Approach
por: Bertsimas, Dimitris, et al.
Publicado: (2023)
por: Bertsimas, Dimitris, et al.
Publicado: (2023)
Sparse Multiple Kernel Learning: Alternating Best Response and Semidefinite Relaxations
por: Bertsimas, Dimitris, et al.
Publicado: (2025)
por: Bertsimas, Dimitris, et al.
Publicado: (2025)
Efficient Domain Adaptation of Multimodal Embeddings using Constrastive Learning
por: Margaritis, Georgios, et al.
Publicado: (2025)
por: Margaritis, Georgios, et al.
Publicado: (2025)
A new perspective on low-rank optimization
por: Bertsimas, Dimitris, et al.
Publicado: (2021)
por: Bertsimas, Dimitris, et al.
Publicado: (2021)
A Multimodal and Explainable Machine Learning Approach to Diagnosing Multi-Class Ejection Fraction from Electrocardiograms
por: Ning, Catherine, et al.
Publicado: (2026)
por: Ning, Catherine, et al.
Publicado: (2026)
TabText: Language-Based Representations of Tabular Health Data for Predictive Modelling
por: Carballo, Kimberly Villalobos, et al.
Publicado: (2022)
por: Carballo, Kimberly Villalobos, et al.
Publicado: (2022)
Holistic Artificial Intelligence in Medicine; improved performance and explainability
por: Petridis, Periklis, et al.
Publicado: (2025)
por: Petridis, Periklis, et al.
Publicado: (2025)
An Interpretable AI Tool for SAVR vs TAVR in Low to Intermediate Risk Patients with Severe Aortic Stenosis
por: Stoumpou, Vasiliki, et al.
Publicado: (2025)
por: Stoumpou, Vasiliki, et al.
Publicado: (2025)
Disjunctive Branch-and-Bound for Certifiably Optimal Low-Rank Matrix Completion
por: Bertsimas, Dimitris, et al.
Publicado: (2023)
por: Bertsimas, Dimitris, et al.
Publicado: (2023)
Robust Tabular Foundation Models
por: Peroni, Matthew, et al.
Publicado: (2025)
por: Peroni, Matthew, et al.
Publicado: (2025)
Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions
por: Oosterhuis, Harrie, et al.
Publicado: (2024)
por: Oosterhuis, Harrie, et al.
Publicado: (2024)
Selecting Interpretability Techniques for Healthcare Machine Learning models
por: Sierra-Botero, Daniel, et al.
Publicado: (2024)
por: Sierra-Botero, Daniel, et al.
Publicado: (2024)
Detection and Localization of Subdural Hematoma Using Deep Learning on Computed Tomography
por: Stoumpou, Vasiliki, et al.
Publicado: (2025)
por: Stoumpou, Vasiliki, et al.
Publicado: (2025)
Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning
por: Deuschel, Jannik, et al.
Publicado: (2023)
por: Deuschel, Jannik, et al.
Publicado: (2023)
Learning When to Switch: Adaptive Policy Selection via Reinforcement Learning
por: Tava, Chris
Publicado: (2025)
por: Tava, Chris
Publicado: (2025)
LCEN: A Nonlinear, Interpretable Feature Selection and Machine Learning Algorithm
por: Seber, Pedro, et al.
Publicado: (2024)
por: Seber, Pedro, et al.
Publicado: (2024)
General Machine Learning Models for Interpreting and Predicting Efficiency Degradation in Organic Solar Cells
por: Valiente, David, et al.
Publicado: (2024)
por: Valiente, David, et al.
Publicado: (2024)
Optimizing Interpretable Decision Tree Policies for Reinforcement Learning
por: Vos, Daniël, et al.
Publicado: (2024)
por: Vos, Daniël, et al.
Publicado: (2024)
Goal-Driven Adaptive Sampling Strategies for Machine Learning Models Predicting Fields
por: Parekh, Jigar, et al.
Publicado: (2026)
por: Parekh, Jigar, et al.
Publicado: (2026)
Interpretable Prediction and Feature Selection for Survival Analysis
por: Van Ness, Mike, et al.
Publicado: (2024)
por: Van Ness, Mike, et al.
Publicado: (2024)
Review of Interpretable Machine Learning Models for Disease Prognosis
por: Shen, Jinzhi, et al.
Publicado: (2024)
por: Shen, Jinzhi, et al.
Publicado: (2024)
Ejemplares similares
-
Deep Trees for (Un)structured Data: Tractability, Performance, and Interpretability
por: Bertsimas, Dimitris, et al.
Publicado: (2024) -
Prescribe-then-Select: Adaptive Policy Selection for Contextual Stochastic Optimization
por: Iglesias, Caio de Prospero, et al.
Publicado: (2025) -
A Machine Learning Approach to Two-Stage Adaptive Robust Optimization
por: Bertsimas, Dimitris, et al.
Publicado: (2023) -
Adaptive Optimization for Prediction with Missing Data
por: Bertsimas, Dimitris, et al.
Publicado: (2024) -
Global Optimization: A Machine Learning Approach
por: Bertsimas, Dimitris, et al.
Publicado: (2023)