Image de couverture de livre

"Over-optimizing" for Normality: Budget-constrained Uncertainty Quantification for Contextual Decision-making

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Wang, Yanyuan, Zhang, Xiaowei
Format:	Preprint
Publié:	2025
Sujets:	Optimization and Control Methodology Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

From Contextual Data to Newsvendor Decisions: On the Actual Performance of Data-Driven Algorithms
par: Besbes, Omar, et autres
Publié: (2023)

Online Resource Allocation with Average Budget Constraints
par: Ao, Ruicheng, et autres
Publié: (2024)

Learning Sequential Decisions from Multiple Sources via Group-Robust Markov Decision Processes
par: Xu, Mingyuan, et autres
Publié: (2026)

HiGrad: Uncertainty Quantification for Online Learning and Stochastic Approximation
par: Su, Weijie J., et autres
Publié: (2018)

Proximal optimal transport divergences
par: Baptista, Ricardo, et autres
Publié: (2025)

SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems
par: Islam, H M Mohaimanul, et autres
Publié: (2024)

Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design
par: Saves, Paul, et autres
Publié: (2025)

A distribution-free mixed-integer optimization approach to hierarchical modelling of clustered and longitudinal data
par: Sankaranarayanan, Madhav, et autres
Publié: (2023)

Decision-Focused Bias Correction for Fluid Approximation
par: Er, Can, et autres
Publié: (2025)

Tensor-on-Tensor Regression: Riemannian Optimization, Over-parameterization, Statistical-computational Gap, and Their Interplay
par: Luo, Yuetian, et autres
Publié: (2022)

Bridging Constraints and Stochasticity: A Fully First-Order Method for Stochastic Bilevel Optimization with Linear Constraints
par: Phan, Cac, et autres
Publié: (2025)

Data-Driven Influence Functions for Optimization-Based Causal Inference
par: Jordan, Michael I., et autres
Publié: (2022)

Contextual Bandits with Budgeted Information Reveal
par: Gan, Kyra, et autres
Publié: (2023)

A Survey of Contextual Optimization Methods for Decision Making under Uncertainty
par: Sadana, Utsav, et autres
Publié: (2023)

AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization
par: Vaswani, Namrata
Publié: (2025)

Global Group Fairness in Federated Learning via Function Tracking
par: Rychener, Yves, et autres
Publié: (2025)

Optimizing Input Data Collection for Ranking and Selection
par: Song, Eunhye, et autres
Publié: (2025)

Calibrated Multi-Level Quantile Forecasting
par: Ding, Tiffany, et autres
Publié: (2025)

Data-Driven Sequential Sampling for Tail Risk Mitigation
par: Ahn, Dohyun, et autres
Publié: (2025)

Automatic Outlier Rectification via Optimal Transport
par: Blanchet, Jose, et autres
Publié: (2024)

Learning Large Causal Structures from Inverse Covariance Matrix via Sparse Matrix Decomposition
par: Dong, Shuyu, et autres
Publié: (2022)

Robust Data-driven Prescriptiveness Optimization
par: Poursoltani, Mehran, et autres
Publié: (2023)

Fast Robust Kernel Regression through Sign Gradient Descent with Early Stopping
par: Allerbo, Oskar
Publié: (2023)

Random Pareto front surfaces
par: Tu, Ben, et autres
Publié: (2024)

Optimal Cross-Validation for Sparse Linear Regression
par: Cory-Wright, Ryan, et autres
Publié: (2023)

Changing the Kernel During Training Leads to Double Descent in Kernel Regression
par: Allerbo, Oskar
Publié: (2023)

DCILP: A Distributed Approach for Large-Scale Causal Structure Learning
par: Dong, Shuyu, et autres
Publié: (2024)

Arc travel time and path choice model estimation subsumed
par: Mohammadpour, Sobhan, et autres
Publié: (2022)

Locally Adaptive Multi-Objective Learning
par: Kaur, Jivat Neet, et autres
Publié: (2026)

Structured Difference-of-Q via Orthogonal Learning
par: Cao, Defu, et autres
Publié: (2024)

The Stochastic Proximal Distance Algorithm
par: Jiang, Haoyu, et autres
Publié: (2022)

Convergence of projected stochastic natural gradient variational inference for various step size and sample or batch size schedules
par: Guilmeau, Thomas, et autres
Publié: (2026)

Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic
par: Sordello, Matteo, et autres
Publié: (2019)

Simultaneous estimation of multiple discrete unimodal distributions under stochastic order constraints
par: Yoshida, Yasuhiro, et autres
Publié: (2026)

StablePCA: Distributionally Robust Learning of Shared Representations from Multi-Source Data
par: Wang, Zhenyu, et autres
Publié: (2025)

Application-Driven Learning: A Closed-Loop Prediction and Optimization Approach Applied to Dynamic Reserves and Demand Forecasting
par: Garcia, Joaquim Dias, et autres
Publié: (2021)

Clustering sequence data with mixture Markov chains with covariates using multiple simplex constrained optimization routine (MSiCOR)
par: Das, Priyam, et autres
Publié: (2016)

Set Invariance with Probability One for Controlled Diffusion: Score-based Approach
par: Wang, Wenqing, et autres
Publié: (2025)

Statistical Performance Guarantee for Subgroup Identification with Generic Machine Learning
par: Li, Michael Lingzhi, et autres
Publié: (2023)

Multi-fidelity Machine Learning for Uncertainty Quantification and Optimization
par: Zhang, Ruda, et autres
Publié: (2024)