Cover Image

A Conformal Approach to Feature-based Newsvendor under Model Misspecification

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Cao, Junyu
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Newsvendor under Ambiguity and Misspecification
by: Liu, Feng, et al.
Published: (2024)

Private Optimal Inventory Policy Learning for Feature-based Newsvendor with Unknown Demand
by: Zhao, Tuoyi, et al.
Published: (2024)

Efficient Imitation under Misspecification
by: Espinosa-Dice, Nicolas, et al.
Published: (2025)

Dynamic Decision-Making under Model Misspecification: A Stochastic Stability Approach
by: Dai, Xinyu, et al.
Published: (2026)

A Probabilistic Approach for Model Alignment with Human Comparisons
by: Cao, Junyu, et al.
Published: (2024)

Thompson Sampling for Repeated Newsvendor
by: Chen, Li, et al.
Published: (2025)

Flow Matching for Robust Simulation-Based Inference under Model Misspecification
by: Ruhlmann, Pierre-Louis, et al.
Published: (2025)

On the Model-Misspecification in Reinforcement Learning
by: Li, Yunfan, et al.
Published: (2023)

No-Regret Linear Bandits under Gap-Adjusted Misspecification
by: Liu, Chong, et al.
Published: (2025)

Decision Making under Model Misspecification: DRO with Robust Bayesian Ambiguity Sets
by: Dellaporta, Charita, et al.
Published: (2025)

The Data-Driven Censored Newsvendor Problem
by: Hssaine, Chamsi, et al.
Published: (2024)

Deep Generative Demand Learning for Newsvendor and Pricing
by: Gong, Shijin, et al.
Published: (2024)

Dissecting the Impact of Model Misspecification in Data-driven Optimization
by: Elmachtoub, Adam N., et al.
Published: (2025)

Gaussian Process Regression under Computational and Epistemic Misspecification
by: Sanz-Alonso, Daniel, et al.
Published: (2023)

Survey of Data-driven Newsvendor: Unified Analysis and Spectrum of Achievable Regrets
by: Chen, Zhuoxin, et al.
Published: (2024)

What is the Value of Censored Data? An Exact Analysis for the Data-driven Newsvendor
by: Kumar, Rachitesh, et al.
Published: (2026)

Rates of Convergence of Generalised Variational Inference Posteriors under Prior Misspecification
by: Mildner, Terje, et al.
Published: (2025)

Representative, Informative, and De-Amplifying: Requirements for Robust Bayesian Active Learning under Model Misspecification
by: Tang, Roubing, et al.
Published: (2025)

Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference
by: Kelly, Ryan P., et al.
Published: (2023)

Addressing Misspecification in Simulation-based Inference through Data-driven Calibration
by: Wehenkel, Antoine, et al.
Published: (2024)

Quantifying the Sensitivity of Inverse Reinforcement Learning to Misspecification
by: Skalse, Joar, et al.
Published: (2024)

Is Elo Rating Reliable? A Study Under Model Misspecification
by: Tang, Shange, et al.
Published: (2025)

Learning When to Restart: Nonstationary Newsvendor from Uncensored to Censored Demand
by: Chen, Xin, et al.
Published: (2025)

Jailbreaking as a Reward Misspecification Problem
by: Xie, Zhihui, et al.
Published: (2024)

Misspecification-robust amortised simulation-based inference using variational methods
by: O'Callaghan, Matthew, et al.
Published: (2025)

Semi-supervised Regression Analysis with Model Misspecification and High-dimensional Data
by: Tian, Ye, et al.
Published: (2024)

Wrong Model, Right Uncertainty: Spatial Associations for Discrete Data with Misspecification
by: Burt, David R., et al.
Published: (2025)

Epistemic Traps: Rational Misalignment Driven by Model Misspecification
by: Xu, Xingcheng, et al.
Published: (2026)

Closing the Gaps: Optimality of Sample Average Approximation for Data-Driven Newsvendor Problems
by: Lyu, Jiameng, et al.
Published: (2024)

Nonasymptotic Regret Analysis of Adaptive Linear Quadratic Control with Model Misspecification
by: Lee, Bruce D., et al.
Published: (2023)

Partial Identifiability and Misspecification in Inverse Reinforcement Learning
by: Skalse, Joar, et al.
Published: (2024)

Universal Batch Learning Under The Misspecification Setting
by: Vituri, Shlomi, et al.
Published: (2024)

The Bias-Variance Tradeoff in Data-Driven Optimization: A Local Misspecification Perspective
by: Lan, Haixiang, et al.
Published: (2025)

Online Incident Response Planning under Model Misspecification through Bayesian Learning and Belief Quantization
by: Hammar, Kim, et al.
Published: (2025)

From Contextual Data to Newsvendor Decisions: On the Actual Performance of Data-Driven Algorithms
by: Besbes, Omar, et al.
Published: (2023)

Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning under Misspecification
by: Rohatgi, Dhruv, et al.
Published: (2025)

Corruption-Robust Linear Bandits: Minimax Optimality and Gap-Dependent Misspecification
by: Liu, Haolin, et al.
Published: (2024)

Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation
by: Schmitt, Marvin, et al.
Published: (2024)

Flow-Controlled Scheduling for LLM Inference with Provable Stability Guarantees
by: Dong, Zhuolun, et al.
Published: (2026)

Feature Qualification by Deep Nets: A Constructive Approach
by: Cao, Feilong, et al.
Published: (2025)