Imagen de Portada

Additive Distributionally Robust Ranking and Selection

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Li, Zaile, Wan, Yuchen, Hong, L. Jeff
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Methodology
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

New Additive OCBA Procedures for Robust Ranking and Selection
por: Wan, Yuchen, et al.
Publicado: (2024)

Efficient Budget Allocation for Large-Scale LLM-Enabled Virtual Screening
por: Li, Zaile, et al.
Publicado: (2024)

The (Surprising) Sample Optimality of Greedy Procedures for Large-Scale Ranking and Selection
por: Li, Zaile, et al.
Publicado: (2023)

AlphaRank: An Artificial Intelligence Approach for Ranking and Selection Problems
por: Zhou, Ruihan, et al.
Publicado: (2024)

Bayesian Additive Distribution Regression
por: Linero, Antonio R., et al.
Publicado: (2026)

UCB for Large-Scale Pure Exploration: Beyond Sub-Gaussianity
por: Li, Zaile, et al.
Publicado: (2025)

Factor Importance Ranking and Selection using Total Indices
por: Huang, Chaofan, et al.
Publicado: (2024)

LLM Sparsity Prior for Robust Feature Selection
por: Skinner, Caleb, et al.
Publicado: (2026)

Learning to Simulate: Generative Metamodeling via Quantile Regression
por: Hong, L. Jeff, et al.
Publicado: (2023)

Sample-Efficient "Clustering and Conquer" Procedures for Parallel Large-Scale Ranking and Selection
por: Zhang, Zishi, et al.
Publicado: (2024)

Flow-based Distributionally Robust Optimization
por: Xu, Chen, et al.
Publicado: (2023)

Multi-Distribution Robust Conformal Prediction
por: Yang, Yuqi, et al.
Publicado: (2026)

Optimal Model Selection for Conformalized Robust Optimization
por: Bao, Yajie, et al.
Publicado: (2025)

Knowledge-Guided Wasserstein Distributionally Robust Optimization
por: Wang, Zitao, et al.
Publicado: (2025)

Optimizing Input Data Collection for Ranking and Selection
por: Song, Eunhye, et al.
Publicado: (2025)

Distribution-Free Robust Predict-Then-Optimize in Function Spaces
por: Patel, Yash, et al.
Publicado: (2026)

Robust Validation: Confident Predictions Even When Distributions Shift
por: Cauchois, Maxime, et al.
Publicado: (2020)

Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation
por: Wang, Zhenyu, et al.
Publicado: (2023)

Multiply Robust Estimation for Local Distribution Shifts with Multiple Domains
por: Wilkins-Reeves, Steven, et al.
Publicado: (2024)

Multiply-Robust Causal Change Attribution
por: Quintas-Martinez, Victor, et al.
Publicado: (2024)

Conditional Rank-Rank Regression via Deep Conditional Transformation Models
por: Wang, Xiaoyi, et al.
Publicado: (2026)

Incorporating Interventional Independence Improves Robustness against Interventional Distribution Shift
por: Sreekumar, Gautam, et al.
Publicado: (2025)

Representation-Aware Distributionally Robust Optimization: A Knowledge Transfer Framework
por: Wang, Zitao, et al.
Publicado: (2025)

Scale-Calibrated Median-of-Means for Robust Distributed Principal Component Analysis
por: You, Kisung
Publicado: (2026)

Towards Identifiable Latent Additive Noise Models
por: Liu, Yuhang, et al.
Publicado: (2024)

Robust Variable Selection for High-dimensional Regression with Missing Data and Measurement Errors
por: Zhang, Zhenhao, et al.
Publicado: (2024)

Effect Decomposition of Functional-Output Computer Experiments via Orthogonal Additive Gaussian Processes
por: Tan, Yu, et al.
Publicado: (2025)

Robust Ultra-High-Dimensional Variable Selection With Correlated Structure Using Group Testing
por: Guo, Wanru, et al.
Publicado: (2026)

Robust Variable Selection and Estimation Via Adaptive Elastic Net S-Estimators for Linear Regression
por: Kepplinger, David
Publicado: (2021)

Analyze Additive and Interaction Effects via Collaborative Trees
por: Chi, Chien-Ming
Publicado: (2024)

BFTS: Thompson Sampling with Bayesian Additive Regression Trees
por: Deng, Ruizhe, et al.
Publicado: (2026)

MLCBART: Multilabel Classification with Bayesian Additive Regression Trees
por: Tian, Jiahao, et al.
Publicado: (2026)

Causal Additive Models with Unobserved Causal Paths and Backdoor Paths
por: Pham, Thong, et al.
Publicado: (2025)

Generalized Criterion for Identifiability of Additive Noise Models Using Majorization
por: Dallakyan, Aramayis, et al.
Publicado: (2024)

Identification of Average Causal Effects in Confounded Additive Noise Models
por: Elahi, Muhammad Qasim, et al.
Publicado: (2024)

Robust and Agnostic Learning of Conditional Distributional Treatment Effects
por: Kallus, Nathan, et al.
Publicado: (2022)

Environment-Adaptive Covariate Selection: Learning When to Use Spurious Correlations for Out-of-Distribution Prediction
por: Zuo, Shuozhi, et al.
Publicado: (2026)

Transductive Conformal Inference for Full Ranking
por: Fermanian, Jean-Baptiste, et al.
Publicado: (2025)

Preference-Based Dynamic Ranking Structure Recognition
por: Lu, Nan, et al.
Publicado: (2025)

Challenges in Variable Importance Ranking Under Correlation
por: Liang, Annie, et al.
Publicado: (2024)