Learning When to Restart: Nonstationary Newsvendor from Uncensored to Censored Demand
Fuente:
arXiv
Saved in:
| Main Authors: | Chen, Xin, Lyu, Jiameng, Yuan, Shilin, Zhou, Yuan |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Closing the Gaps: Optimality of Sample Average Approximation for Data-Driven Newsvendor Problems
by: Lyu, Jiameng, et al.
Published: (2024)
by: Lyu, Jiameng, et al.
Published: (2024)
A Minibatch-SGD-Based Learning Meta-Policy for Inventory Systems with Myopic Optimal Policy
by: Lyu, Jiameng, et al.
Published: (2024)
by: Lyu, Jiameng, et al.
Published: (2024)
The Data-Driven Censored Newsvendor Problem
by: Hssaine, Chamsi, et al.
Published: (2024)
by: Hssaine, Chamsi, et al.
Published: (2024)
Deep Generative Demand Learning for Newsvendor and Pricing
by: Gong, Shijin, et al.
Published: (2024)
by: Gong, Shijin, et al.
Published: (2024)
The Nonstationary Newsvendor with (and without) Predictions
by: An, Lin, et al.
Published: (2023)
by: An, Lin, et al.
Published: (2023)
Spatial Supply Repositioning with Censored Demand Data
by: Jiang, Hansheng, et al.
Published: (2025)
by: Jiang, Hansheng, et al.
Published: (2025)
A Minimax-MDP Framework with Future-imposed Conditions for Learning-augmented Problems
by: Chen, Xin, et al.
Published: (2025)
by: Chen, Xin, et al.
Published: (2025)
Restarted contractive operators to learn at equilibrium
by: Davy, Leo, et al.
Published: (2025)
by: Davy, Leo, et al.
Published: (2025)
In-Context Learning for Data-Driven Censored Inventory Control
by: Mukherjee, Sohom, et al.
Published: (2026)
by: Mukherjee, Sohom, et al.
Published: (2026)
From Contextual Data to Newsvendor Decisions: On the Actual Performance of Data-Driven Algorithms
by: Besbes, Omar, et al.
Published: (2023)
by: Besbes, Omar, et al.
Published: (2023)
On the Provable Suboptimality of Momentum SGD in Nonstationary Stochastic Optimization
by: Sahu, Sharan, et al.
Published: (2026)
by: Sahu, Sharan, et al.
Published: (2026)
Explore-then-Commit for Nonstationary Linear Bandits with Latent Dynamics
by: Choi, Sunmook, et al.
Published: (2025)
by: Choi, Sunmook, et al.
Published: (2025)
AdaSwitch: An Adaptive Switching Meta-Algorithm for Learning-Augmented Bounded-Influence Problems
by: Chen, Xi, et al.
Published: (2025)
by: Chen, Xi, et al.
Published: (2025)
MARS-M: When Variance Reduction Meets Matrices
by: Liu, Yifeng, et al.
Published: (2025)
by: Liu, Yifeng, et al.
Published: (2025)
Policy Transfer for Continuous-Time Reinforcement Learning: A (Rough) Differential Equation Approach
by: Guo, Xin, et al.
Published: (2025)
by: Guo, Xin, et al.
Published: (2025)
Optimal and Order-optimal Gated Priority-based Greedy Policies for Two-layer Multi-item Order Fulfillment
by: Chen, Xi, et al.
Published: (2026)
by: Chen, Xi, et al.
Published: (2026)
Power Constrained Nonstationary Bandits with Habituation and Recovery Dynamics
by: Li, Fengxu, et al.
Published: (2025)
by: Li, Fengxu, et al.
Published: (2025)
Online Learning and Optimization for Queues with Unknown Demand Curve and Service Distribution
by: Chen, Xinyun, et al.
Published: (2023)
by: Chen, Xinyun, et al.
Published: (2023)
Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control
by: Jungel, Kai, et al.
Published: (2023)
by: Jungel, Kai, et al.
Published: (2023)
A Re-solving Heuristic for Dynamic Assortment Optimization with Knapsack Constraints
by: Chen, Xi, et al.
Published: (2024)
by: Chen, Xi, et al.
Published: (2024)
Towards Simple and Provable Parameter-Free Adaptive Gradient Methods
by: Tao, Yuanzhe, et al.
Published: (2024)
by: Tao, Yuanzhe, et al.
Published: (2024)
Greedy Low-Rank Gradient Compression for Distributed Learning with Convergence Guarantees
by: Chen, Chuyan, et al.
Published: (2025)
by: Chen, Chuyan, et al.
Published: (2025)
Efficient First-Order Optimization on the Pareto Set for Multi-Objective Learning under Preference Guidance
by: Chen, Lisha, et al.
Published: (2025)
by: Chen, Lisha, et al.
Published: (2025)
On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization
by: Zhou, Dongruo, et al.
Published: (2018)
by: Zhou, Dongruo, et al.
Published: (2018)
Reconstructing Physics-Informed Machine Learning for Traffic Flow Modeling: a Multi-Gradient Descent and Pareto Learning Approach
by: Lei, Yuan-Zheng, et al.
Published: (2025)
by: Lei, Yuan-Zheng, et al.
Published: (2025)
A Reinforcement-Learning-Based Multiple-Column Selection Strategy for Column Generation
by: Yuan, Haofeng, et al.
Published: (2023)
by: Yuan, Haofeng, et al.
Published: (2023)
The ADMM-PINNs Algorithmic Framework for Nonsmooth PDE-Constrained Optimization: A Deep Learning Approach
by: Song, Yongcun, et al.
Published: (2023)
by: Song, Yongcun, et al.
Published: (2023)
When Deep Learning Meets Polyhedral Theory: A Survey
by: Huchette, Joey, et al.
Published: (2023)
by: Huchette, Joey, et al.
Published: (2023)
From PowerSGD to PowerSGD+: Low-Rank Gradient Compression for Distributed Optimization with Convergence Guarantees
by: Xie, Shengping, et al.
Published: (2025)
by: Xie, Shengping, et al.
Published: (2025)
Semi-on-Demand Transit Feeders with Shared Autonomous Vehicles and Reinforcement-Learning-Based Zonal Dispatching Control
by: Ng, Max T. M., et al.
Published: (2025)
by: Ng, Max T. M., et al.
Published: (2025)
Navigating Demand Uncertainty in Container Shipping: Deep Reinforcement Learning for Enabling Adaptive and Feasible Master Stowage Planning
by: van Twiller, Jaike, et al.
Published: (2025)
by: van Twiller, Jaike, et al.
Published: (2025)
Transit Network Design with Two-Level Demand Uncertainties: A Machine Learning and Contextual Stochastic Optimization Framework
by: Guan, Hongzhao, et al.
Published: (2026)
by: Guan, Hongzhao, et al.
Published: (2026)
Momentum Benefits Non-IID Federated Learning Simply and Provably
by: Cheng, Ziheng, et al.
Published: (2023)
by: Cheng, Ziheng, et al.
Published: (2023)
Newsvendor under Ambiguity and Misspecification
by: Liu, Feng, et al.
Published: (2024)
by: Liu, Feng, et al.
Published: (2024)
When Descent Is Too Stable: Event-Triggered Hamiltonian Learning to Optimize
by: Wang, Yi, et al.
Published: (2026)
by: Wang, Yi, et al.
Published: (2026)
A Distributed ADMM-based Deep Learning Approach for Thermal Control in Multi-Zone Buildings under Demand Response Events
by: Taboga, Vincent, et al.
Published: (2023)
by: Taboga, Vincent, et al.
Published: (2023)
Deep Neural Newsvendor
by: Han, Jinhui, et al.
Published: (2023)
by: Han, Jinhui, et al.
Published: (2023)
MARS: Unleashing the Power of Variance Reduction for Training Large Models
by: Yuan, Huizhuo, et al.
Published: (2024)
by: Yuan, Huizhuo, et al.
Published: (2024)
When Machine Learning Meets Importance Sampling: A More Efficient Rare Event Estimation Approach
by: Zhao, Ruoning, et al.
Published: (2025)
by: Zhao, Ruoning, et al.
Published: (2025)
The Marginal Value of Momentum for Small Learning Rate SGD
by: Wang, Runzhe, et al.
Published: (2023)
by: Wang, Runzhe, et al.
Published: (2023)
Similar Items
-
Closing the Gaps: Optimality of Sample Average Approximation for Data-Driven Newsvendor Problems
by: Lyu, Jiameng, et al.
Published: (2024) -
A Minibatch-SGD-Based Learning Meta-Policy for Inventory Systems with Myopic Optimal Policy
by: Lyu, Jiameng, et al.
Published: (2024) -
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) -
The Nonstationary Newsvendor with (and without) Predictions
by: An, Lin, et al.
Published: (2023)